diff --git a/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/.ipynb_checkpoints/C3_W2_Collaborative_RecSys_Assignment-checkpoint.ipynb b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/.ipynb_checkpoints/C3_W2_Collaborative_RecSys_Assignment-checkpoint.ipynb new file mode 100644 index 00000000..ec446e51 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/.ipynb_checkpoints/C3_W2_Collaborative_RecSys_Assignment-checkpoint.ipynb @@ -0,0 +1,789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Lzk7iX_CodX6", + "tags": [] + }, + "source": [ + "# Practice lab: Collaborative Filtering Recommender Systems\n", + "\n", + "In this exercise, you will implement collaborative filtering to build a recommender system for movies. \n", + "\n", + "# Outline\n", + "- [ 1 - Notation](#1)\n", + "- [ 2 - Recommender Systems](#2)\n", + "- [ 3 - Movie ratings dataset](#3)\n", + "- [ 4 - Collaborative filtering learning algorithm](#4)\n", + " - [ 4.1 Collaborative filtering cost function](#4.1)\n", + " - [ Exercise 1](#ex01)\n", + "- [ 5 - Learning movie recommendations](#5)\n", + "- [ 6 - Recommendations](#6)\n", + "- [ 7 - Congratulations!](#7)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Packages \n", + "We will use the now familiar NumPy and Tensorflow Packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from recsys_utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 - Notation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|General
Notation | Description| Python (if any) |\n", + "|:-------------|:------------------------------------------------------------||\n", + "| $r(i,j)$ | scalar; = 1 if user j rated game i = 0 otherwise ||\n", + "| $y(i,j)$ | scalar; = rating given by user j on game i (if r(i,j) = 1 is defined) ||\n", + "|$\\mathbf{w}^{(j)}$ | vector; parameters for user j ||\n", + "|$b^{(j)}$ | scalar; parameter for user j ||\n", + "| $\\mathbf{x}^{(i)}$ | vector; feature ratings for movie i || \n", + "| $n_u$ | number of users |num_users|\n", + "| $n_m$ | number of movies | num_movies |\n", + "| $n$ | number of features | num_features |\n", + "| $\\mathbf{X}$ | matrix of vectors $\\mathbf{x}^{(i)}$ | X |\n", + "| $\\mathbf{W}$ | matrix of vectors $\\mathbf{w}^{(j)}$ | W |\n", + "| $\\mathbf{b}$ | vector of bias parameters $b^{(j)}$ | b |\n", + "| $\\mathbf{R}$ | matrix of elements $r(i,j)$ | R |\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "\n", + "## 2 - Recommender Systems \n", + "In this lab, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings.\n", + "The goal of a collaborative filtering recommender system is to generate two vectors: For each user, a 'parameter vector' that embodies the movie tastes of a user. For each movie, a feature vector of the same size which embodies some description of the movie. The dot product of the two vectors plus the bias term should produce an estimate of the rating the user might give to that movie.\n", + "\n", + "The diagram below details how these vectors are learned." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Existing ratings are provided in matrix form as shown. $Y$ contains ratings; 0.5 to 5 inclusive in 0.5 steps. 0 if the movie has not been rated. $R$ has a 1 where movies have been rated. Movies are in rows, users in columns. Each user has a parameter vector $w^{user}$ and bias. Each movie has a feature vector $x^{movie}$. These vectors are simultaneously learned by using the existing user/movie ratings as training data. One training example is shown above: $\\mathbf{w}^{(1)} \\cdot \\mathbf{x}^{(1)} + b^{(1)} = 4$. It is worth noting that the feature vector $x^{movie}$ must satisfy all the users while the user vector $w^{user}$ must satisfy all the movies. This is the source of the name of this approach - all the users collaborate to generate the rating set. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the feature vectors and parameters are learned, they can be used to predict how a user might rate an unrated movie. This is shown in the diagram above. The equation is an example of predicting a rating for user one on movie zero." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "In this exercise, you will implement the function `cofiCostFunc` that computes the collaborative filtering\n", + "objective function. After implementing the objective function, you will use a TensorFlow custom training loop to learn the parameters for collaborative filtering. The first step is to detail the data set and data structures that will be used in the lab." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6-09Hto6odYD" + }, + "source": [ + "\n", + "## 3 - Movie ratings dataset \n", + "The data set is derived from the [MovieLens \"ml-latest-small\"](https://grouplens.org/datasets/movielens/latest/) dataset. \n", + "[F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. ]\n", + "\n", + "The original dataset has 9000 movies rated by 600 users. The dataset has been reduced in size to focus on movies from the years since 2000. This dataset consists of ratings on a scale of 0.5 to 5 in 0.5 step increments. The reduced dataset has $n_u = 443$ users, and $n_m= 4778$ movies. \n", + "\n", + "Below, you will load the movie dataset into the variables $Y$ and $R$.\n", + "\n", + "The matrix $Y$ (a $n_m \\times n_u$ matrix) stores the ratings $y^{(i,j)}$. The matrix $R$ is an binary-valued indicator matrix, where $R(i,j) = 1$ if user $j$ gave a rating to movie $i$, and $R(i,j)=0$ otherwise. \n", + "\n", + "Throughout this part of the exercise, you will also be working with the\n", + "matrices, $\\mathbf{X}$, $\\mathbf{W}$ and $\\mathbf{b}$: \n", + "\n", + "$$\\mathbf{X} = \n", + "\\begin{bmatrix}\n", + "--- (\\mathbf{x}^{(0)})^T --- \\\\\n", + "--- (\\mathbf{x}^{(1)})^T --- \\\\\n", + "\\vdots \\\\\n", + "--- (\\mathbf{x}^{(n_m-1)})^T --- \\\\\n", + "\\end{bmatrix} , \\quad\n", + "\\mathbf{W} = \n", + "\\begin{bmatrix}\n", + "--- (\\mathbf{w}^{(0)})^T --- \\\\\n", + "--- (\\mathbf{w}^{(1)})^T --- \\\\\n", + "\\vdots \\\\\n", + "--- (\\mathbf{w}^{(n_u-1)})^T --- \\\\\n", + "\\end{bmatrix},\\quad\n", + "\\mathbf{ b} = \n", + "\\begin{bmatrix}\n", + " b^{(0)} \\\\\n", + " b^{(1)} \\\\\n", + "\\vdots \\\\\n", + "b^{(n_u-1)} \\\\\n", + "\\end{bmatrix}\\quad\n", + "$$ \n", + "\n", + "The $i$-th row of $\\mathbf{X}$ corresponds to the\n", + "feature vector $x^{(i)}$ for the $i$-th movie, and the $j$-th row of\n", + "$\\mathbf{W}$ corresponds to one parameter vector $\\mathbf{w}^{(j)}$, for the\n", + "$j$-th user. Both $x^{(i)}$ and $\\mathbf{w}^{(j)}$ are $n$-dimensional\n", + "vectors. For the purposes of this exercise, you will use $n=10$, and\n", + "therefore, $\\mathbf{x}^{(i)}$ and $\\mathbf{w}^{(j)}$ have 10 elements.\n", + "Correspondingly, $\\mathbf{X}$ is a\n", + "$n_m \\times 10$ matrix and $\\mathbf{W}$ is a $n_u \\times 10$ matrix.\n", + "\n", + "We will start by loading the movie ratings dataset to understand the structure of the data.\n", + "We will load $Y$ and $R$ with the movie dataset. \n", + "We'll also load $\\mathbf{X}$, $\\mathbf{W}$, and $\\mathbf{b}$ with pre-computed values. These values will be learned later in the lab, but we'll use pre-computed values to develop the cost model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Load data\n", + "X, W, b, num_movies, num_features, num_users = load_precalc_params_small()\n", + "Y, R = load_ratings_small()\n", + "\n", + "print(\"Y\", Y.shape, \"R\", R.shape)\n", + "print(\"X\", X.shape)\n", + "print(\"W\", W.shape)\n", + "print(\"b\", b.shape)\n", + "print(\"num_features\", num_features)\n", + "print(\"num_movies\", num_movies)\n", + "print(\"num_users\", num_users)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bxm1O_wbodYF" + }, + "outputs": [], + "source": [ + "# From the matrix, we can compute statistics like average rating.\n", + "tsmean = np.mean(Y[0, R[0, :].astype(bool)])\n", + "print(f\"Average rating for movie 1 : {tsmean:0.3f} / 5\" )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4 - Collaborative filtering learning algorithm \n", + "\n", + "Now, you will begin implementing the collaborative filtering learning\n", + "algorithm. You will start by implementing the objective function. \n", + "\n", + "The collaborative filtering algorithm in the setting of movie\n", + "recommendations considers a set of $n$-dimensional parameter vectors\n", + "$\\mathbf{x}^{(0)},...,\\mathbf{x}^{(n_m-1)}$, $\\mathbf{w}^{(0)},...,\\mathbf{w}^{(n_u-1)}$ and $b^{(0)},...,b^{(n_u-1)}$, where the\n", + "model predicts the rating for movie $i$ by user $j$ as\n", + "$y^{(i,j)} = \\mathbf{w}^{(j)}\\cdot \\mathbf{x}^{(i)} + b^{(i)}$ . Given a dataset that consists of\n", + "a set of ratings produced by some users on some movies, you wish to\n", + "learn the parameter vectors $\\mathbf{x}^{(0)},...,\\mathbf{x}^{(n_m-1)},\n", + "\\mathbf{w}^{(0)},...,\\mathbf{w}^{(n_u-1)}$ and $b^{(0)},...,b^{(n_u-1)}$ that produce the best fit (minimizes\n", + "the squared error).\n", + "\n", + "You will complete the code in cofiCostFunc to compute the cost\n", + "function for collaborative filtering. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bcqg0LJWodYH" + }, + "source": [ + "\n", + "\n", + "### 4.1 Collaborative filtering cost function\n", + "\n", + "The collaborative filtering cost function is given by\n", + "$$J({\\mathbf{x}^{(0)},...,\\mathbf{x}^{(n_m-1)},\\mathbf{w}^{(0)},b^{(0)},...,\\mathbf{w}^{(n_u-1)},b^{(n_u-1)}})= \\frac{1}{2}\\sum_{(i,j):r(i,j)=1}(\\mathbf{w}^{(j)} \\cdot \\mathbf{x}^{(i)} + b^{(j)} - y^{(i,j)})^2\n", + "+\\underbrace{\n", + "\\frac{\\lambda}{2}\n", + "\\sum_{j=0}^{n_u-1}\\sum_{k=0}^{n-1}(\\mathbf{w}^{(j)}_k)^2\n", + "+ \\frac{\\lambda}{2}\\sum_{i=0}^{n_m-1}\\sum_{k=0}^{n-1}(\\mathbf{x}_k^{(i)})^2\n", + "}_{regularization}\n", + "\\tag{1}$$\n", + "The first summation in (1) is \"for all $i$, $j$ where $r(i,j)$ equals $1$\" and could be written:\n", + "\n", + "$$\n", + "= \\frac{1}{2}\\sum_{j=0}^{n_u-1} \\sum_{i=0}^{n_m-1}r(i,j)*(\\mathbf{w}^{(j)} \\cdot \\mathbf{x}^{(i)} + b^{(j)} - y^{(i,j)})^2\n", + "+\\text{regularization}\n", + "$$\n", + "\n", + "You should now write cofiCostFunc (collaborative filtering cost function) to return this cost." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 1\n", + "\n", + "**For loop Implementation:** \n", + "Start by implementing the cost function using for loops.\n", + "Consider developing the cost function in two steps. First, develop the cost function without regularization. A test case that does not include regularization is provided below to test your implementation. Once that is working, add regularization and run the tests that include regularization. Note that you should be accumulating the cost for user $j$ and movie $i$ only if $R(i,j) = 1$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: cofi_cost_func\n", + "# UNQ_C1\n", + "\n", + "def cofi_cost_func(X, W, b, Y, R, lambda_):\n", + " \"\"\"\n", + " Returns the cost for the content-based filtering\n", + " Args:\n", + " X (ndarray (num_movies,num_features)): matrix of item features\n", + " W (ndarray (num_users,num_features)) : matrix of user parameters\n", + " b (ndarray (1, num_users) : vector of user parameters\n", + " Y (ndarray (num_movies,num_users) : matrix of user ratings of movies\n", + " R (ndarray (num_movies,num_users) : matrix, where R(i, j) = 1 if the i-th movies was rated by the j-th user\n", + " lambda_ (float): regularization parameter\n", + " Returns:\n", + " J (float) : Cost\n", + " \"\"\"\n", + " nm, nu = Y.shape\n", + " J = 0\n", + " ### START CODE HERE ### \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " ### END CODE HERE ### \n", + "\n", + " return J" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Public tests\n", + "from public_tests import *\n", + "test_cofi_cost_func(cofi_cost_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " You can structure the code in two for loops similar to the summation in (1). \n", + " Implement the code without regularization first. \n", + " Note that some of the elements in (1) are vectors. Use np.dot(). You can also use np.square().\n", + " Pay close attention to which elements are indexed by i and which are indexed by j. Don't forget to divide by two.\n", + " \n", + "```python \n", + " ### START CODE HERE ### \n", + " for j in range(nu):\n", + " \n", + " \n", + " for i in range(nm):\n", + " \n", + " \n", + " ### END CODE HERE ### \n", + "``` \n", + "
\n", + " Click for more hints\n", + " \n", + " Here is some more details. The code below pulls out each element from the matrix before using it. \n", + " One could also reference the matrix directly. \n", + " This code does not contain regularization.\n", + " \n", + "```python \n", + " nm,nu = Y.shape\n", + " J = 0\n", + " ### START CODE HERE ### \n", + " for j in range(nu):\n", + " w = W[j,:]\n", + " b_j = b[0,j]\n", + " for i in range(nm):\n", + " x = \n", + " y = \n", + " r =\n", + " J += \n", + " J = J/2\n", + " ### END CODE HERE ### \n", + "\n", + "```\n", + " \n", + "
\n", + " Last Resort (full non-regularized implementation)\n", + " \n", + "```python \n", + " nm,nu = Y.shape\n", + " J = 0\n", + " ### START CODE HERE ### \n", + " for j in range(nu):\n", + " w = W[j,:]\n", + " b_j = b[0,j]\n", + " for i in range(nm):\n", + " x = X[i,:]\n", + " y = Y[i,j]\n", + " r = R[i,j]\n", + " J += np.square(r * (np.dot(w,x) + b_j - y ) )\n", + " J = J/2\n", + " ### END CODE HERE ### \n", + "```\n", + " \n", + "
\n", + " regularization\n", + " Regularization just squares each element of the W array and X array and them sums all the squared elements.\n", + " You can utilize np.square() and np.sum().\n", + "\n", + "
\n", + " regularization details\n", + " \n", + "```python \n", + " J += lambda_* (np.sum(np.square(W)) + np.sum(np.square(X)))\n", + "```\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reduce the data set size so that this runs faster\n", + "num_users_r = 4\n", + "num_movies_r = 5 \n", + "num_features_r = 3\n", + "\n", + "X_r = X[:num_movies_r, :num_features_r]\n", + "W_r = W[:num_users_r, :num_features_r]\n", + "b_r = b[0, :num_users_r].reshape(1,-1)\n", + "Y_r = Y[:num_movies_r, :num_users_r]\n", + "R_r = R[:num_movies_r, :num_users_r]\n", + "\n", + "# Evaluate cost function\n", + "J = cofi_cost_func(X_r, W_r, b_r, Y_r, R_r, 0);\n", + "print(f\"Cost: {J:0.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xGznmQ91odYL" + }, + "source": [ + "**Expected Output (lambda = 0)**: \n", + "$13.67$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate cost function with regularization \n", + "J = cofi_cost_func(X_r, W_r, b_r, Y_r, R_r, 1.5);\n", + "print(f\"Cost (with regularization): {J:0.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1xbepzUUodYP" + }, + "source": [ + "**Expected Output**:\n", + "\n", + "28.09" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Vectorized Implementation**\n", + "\n", + "It is important to create a vectorized implementation to compute $J$, since it will later be called many times during optimization. The linear algebra utilized is not the focus of this series, so the implementation is provided. If you are an expert in linear algebra, feel free to create your version without referencing the code below. \n", + "\n", + "Run the code below and verify that it produces the same results as the non-vectorized version." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cofi_cost_func_v(X, W, b, Y, R, lambda_):\n", + " \"\"\"\n", + " Returns the cost for the content-based filtering\n", + " Vectorized for speed. Uses tensorflow operations to be compatible with custom training loop.\n", + " Args:\n", + " X (ndarray (num_movies,num_features)): matrix of item features\n", + " W (ndarray (num_users,num_features)) : matrix of user parameters\n", + " b (ndarray (1, num_users) : vector of user parameters\n", + " Y (ndarray (num_movies,num_users) : matrix of user ratings of movies\n", + " R (ndarray (num_movies,num_users) : matrix, where R(i, j) = 1 if the i-th movies was rated by the j-th user\n", + " lambda_ (float): regularization parameter\n", + " Returns:\n", + " J (float) : Cost\n", + " \"\"\"\n", + " j = (tf.linalg.matmul(X, tf.transpose(W)) + b - Y)*R\n", + " J = 0.5 * tf.reduce_sum(j**2) + (lambda_/2) * (tf.reduce_sum(X**2) + tf.reduce_sum(W**2))\n", + " return J" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate cost function\n", + "J = cofi_cost_func_v(X_r, W_r, b_r, Y_r, R_r, 0);\n", + "print(f\"Cost: {J:0.2f}\")\n", + "\n", + "# Evaluate cost function with regularization \n", + "J = cofi_cost_func_v(X_r, W_r, b_r, Y_r, R_r, 1.5);\n", + "print(f\"Cost (with regularization): {J:0.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1xbepzUUodYP" + }, + "source": [ + "**Expected Output**: \n", + "Cost: 13.67 \n", + "Cost (with regularization): 28.09" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ilaeM8yWodYR" + }, + "source": [ + "\n", + "## 5 - Learning movie recommendations \n", + "------------------------------\n", + "\n", + "After you have finished implementing the collaborative filtering cost\n", + "function, you can start training your algorithm to make\n", + "movie recommendations for yourself. \n", + "\n", + "In the cell below, you can enter your own movie choices. The algorithm will then make recommendations for you! We have filled out some values according to our preferences, but after you have things working with our choices, you should change this to match your tastes.\n", + "A list of all movies in the dataset is in the file [movie list](data/small_movie_list.csv)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WJO8Jr0UodYR" + }, + "outputs": [], + "source": [ + "movieList, movieList_df = load_Movie_List_pd()\n", + "\n", + "my_ratings = np.zeros(num_movies) # Initialize my ratings\n", + "\n", + "# Check the file small_movie_list.csv for id of each movie in our dataset\n", + "# For example, Toy Story 3 (2010) has ID 2700, so to rate it \"5\", you can set\n", + "my_ratings[2700] = 5 \n", + "\n", + "#Or suppose you did not enjoy Persuasion (2007), you can set\n", + "my_ratings[2609] = 2;\n", + "\n", + "# We have selected a few movies we liked / did not like and the ratings we\n", + "# gave are as follows:\n", + "my_ratings[929] = 5 # Lord of the Rings: The Return of the King, The\n", + "my_ratings[246] = 5 # Shrek (2001)\n", + "my_ratings[2716] = 3 # Inception\n", + "my_ratings[1150] = 5 # Incredibles, The (2004)\n", + "my_ratings[382] = 2 # Amelie (Fabuleux destin d'Amélie Poulain, Le)\n", + "my_ratings[366] = 5 # Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\n", + "my_ratings[622] = 5 # Harry Potter and the Chamber of Secrets (2002)\n", + "my_ratings[988] = 3 # Eternal Sunshine of the Spotless Mind (2004)\n", + "my_ratings[2925] = 1 # Louis Theroux: Law & Disorder (2008)\n", + "my_ratings[2937] = 1 # Nothing to Declare (Rien à déclarer)\n", + "my_ratings[793] = 5 # Pirates of the Caribbean: The Curse of the Black Pearl (2003)\n", + "my_rated = [i for i in range(len(my_ratings)) if my_ratings[i] > 0]\n", + "\n", + "print('\\nNew user ratings:\\n')\n", + "for i in range(len(my_ratings)):\n", + " if my_ratings[i] > 0 :\n", + " print(f'Rated {my_ratings[i]} for {movieList_df.loc[i,\"title\"]}');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's add these reviews to $Y$ and $R$ and normalize the ratings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reload ratings and add new ratings\n", + "Y, R = load_ratings_small()\n", + "Y = np.c_[my_ratings, Y]\n", + "R = np.c_[(my_ratings != 0).astype(int), R]\n", + "\n", + "# Normalize the Dataset\n", + "Ynorm, Ymean = normalizeRatings(Y, R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's prepare to train the model. Initialize the parameters and select the Adam optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Useful Values\n", + "num_movies, num_users = Y.shape\n", + "num_features = 100\n", + "\n", + "# Set Initial Parameters (W, X), use tf.Variable to track these variables\n", + "tf.random.set_seed(1234) # for consistent results\n", + "W = tf.Variable(tf.random.normal((num_users, num_features),dtype=tf.float64), name='W')\n", + "X = tf.Variable(tf.random.normal((num_movies, num_features),dtype=tf.float64), name='X')\n", + "b = tf.Variable(tf.random.normal((1, num_users), dtype=tf.float64), name='b')\n", + "\n", + "# Instantiate an optimizer.\n", + "optimizer = keras.optimizers.Adam(learning_rate=1e-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now train the collaborative filtering model. This will learn the parameters $\\mathbf{X}$, $\\mathbf{W}$, and $\\mathbf{b}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The operations involved in learning $w$, $b$, and $x$ simultaneously do not fall into the typical 'layers' offered in the TensorFlow neural network package. Consequently, the flow used in Course 2: Model, Compile(), Fit(), Predict(), are not directly applicable. Instead, we can use a custom training loop.\n", + "\n", + "Recall from earlier labs the steps of gradient descent.\n", + "- repeat until convergence:\n", + " - compute forward pass\n", + " - compute the derivatives of the loss relative to parameters\n", + " - update the parameters using the learning rate and the computed derivatives \n", + " \n", + "TensorFlow has the marvelous capability of calculating the derivatives for you. This is shown below. Within the `tf.GradientTape()` section, operations on Tensorflow Variables are tracked. When `tape.gradient()` is later called, it will return the gradient of the loss relative to the tracked variables. The gradients can then be applied to the parameters using an optimizer. \n", + "This is a very brief introduction to a useful feature of TensorFlow and other machine learning frameworks. Further information can be found by investigating \"custom training loops\" within the framework of interest.\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iterations = 200\n", + "lambda_ = 1\n", + "for iter in range(iterations):\n", + " # Use TensorFlow’s GradientTape\n", + " # to record the operations used to compute the cost \n", + " with tf.GradientTape() as tape:\n", + "\n", + " # Compute the cost (forward pass included in cost)\n", + " cost_value = cofi_cost_func_v(X, W, b, Ynorm, R, lambda_)\n", + "\n", + " # Use the gradient tape to automatically retrieve\n", + " # the gradients of the trainable variables with respect to the loss\n", + " grads = tape.gradient( cost_value, [X,W,b] )\n", + "\n", + " # Run one step of gradient descent by updating\n", + " # the value of the variables to minimize the loss.\n", + " optimizer.apply_gradients( zip(grads, [X,W,b]) )\n", + "\n", + " # Log periodically.\n", + " if iter % 20 == 0:\n", + " print(f\"Training loss at iteration {iter}: {cost_value:0.1f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SSzUL7eQodYS" + }, + "source": [ + "\n", + "## 6 - Recommendations\n", + "Below, we compute the ratings for all the movies and users and display the movies that are recommended. These are based on the movies and ratings entered as `my_ratings[]` above. To predict the rating of movie $i$ for user $j$, you compute $\\mathbf{w}^{(j)} \\cdot \\mathbf{x}^{(i)} + b^{(j)}$. This can be computed for all ratings using matrix multiplication." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ns266wKtodYT" + }, + "outputs": [], + "source": [ + "# Make a prediction using trained weights and biases\n", + "p = np.matmul(X.numpy(), np.transpose(W.numpy())) + b.numpy()\n", + "\n", + "#restore the mean\n", + "pm = p + Ymean\n", + "\n", + "my_predictions = pm[:,0]\n", + "\n", + "# sort predictions\n", + "ix = tf.argsort(my_predictions, direction='DESCENDING')\n", + "\n", + "for i in range(17):\n", + " j = ix[i]\n", + " if j not in my_rated:\n", + " print(f'Predicting rating {my_predictions[j]:0.2f} for movie {movieList[j]}')\n", + "\n", + "print('\\n\\nOriginal vs Predicted ratings:\\n')\n", + "for i in range(len(my_ratings)):\n", + " if my_ratings[i] > 0:\n", + " print(f'Original {my_ratings[i]}, Predicted {my_predictions[i]:0.2f} for {movieList[i]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In practice, additional information can be utilized to enhance our predictions. Above, the predicted ratings for the first few hundred movies lie in a small range. We can augment the above by selecting from those top movies, movies that have high average ratings and movies with more than 20 ratings. This section uses a [Pandas](https://pandas.pydata.org/) data frame which has many handy sorting features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filter=(movieList_df[\"number of ratings\"] > 20)\n", + "movieList_df[\"pred\"] = my_predictions\n", + "movieList_df = movieList_df.reindex(columns=[\"pred\", \"mean rating\", \"number of ratings\", \"title\"])\n", + "movieList_df.loc[ix[:300]].loc[filter].sort_values(\"mean rating\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7 - Congratulations! \n", + "You have implemented a useful recommender system!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/C3_W2_Collaborative_RecSys_Assignment.ipynb b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/C3_W2_Collaborative_RecSys_Assignment.ipynb new file mode 100644 index 00000000..ec446e51 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/C3_W2_Collaborative_RecSys_Assignment.ipynb @@ -0,0 +1,789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Lzk7iX_CodX6", + "tags": [] + }, + "source": [ + "# Practice lab: Collaborative Filtering Recommender Systems\n", + "\n", + "In this exercise, you will implement collaborative filtering to build a recommender system for movies. \n", + "\n", + "# Outline\n", + "- [ 1 - Notation](#1)\n", + "- [ 2 - Recommender Systems](#2)\n", + "- [ 3 - Movie ratings dataset](#3)\n", + "- [ 4 - Collaborative filtering learning algorithm](#4)\n", + " - [ 4.1 Collaborative filtering cost function](#4.1)\n", + " - [ Exercise 1](#ex01)\n", + "- [ 5 - Learning movie recommendations](#5)\n", + "- [ 6 - Recommendations](#6)\n", + "- [ 7 - Congratulations!](#7)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Packages \n", + "We will use the now familiar NumPy and Tensorflow Packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from recsys_utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 - Notation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|General
Notation | Description| Python (if any) |\n", + "|:-------------|:------------------------------------------------------------||\n", + "| $r(i,j)$ | scalar; = 1 if user j rated game i = 0 otherwise ||\n", + "| $y(i,j)$ | scalar; = rating given by user j on game i (if r(i,j) = 1 is defined) ||\n", + "|$\\mathbf{w}^{(j)}$ | vector; parameters for user j ||\n", + "|$b^{(j)}$ | scalar; parameter for user j ||\n", + "| $\\mathbf{x}^{(i)}$ | vector; feature ratings for movie i || \n", + "| $n_u$ | number of users |num_users|\n", + "| $n_m$ | number of movies | num_movies |\n", + "| $n$ | number of features | num_features |\n", + "| $\\mathbf{X}$ | matrix of vectors $\\mathbf{x}^{(i)}$ | X |\n", + "| $\\mathbf{W}$ | matrix of vectors $\\mathbf{w}^{(j)}$ | W |\n", + "| $\\mathbf{b}$ | vector of bias parameters $b^{(j)}$ | b |\n", + "| $\\mathbf{R}$ | matrix of elements $r(i,j)$ | R |\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "\n", + "## 2 - Recommender Systems \n", + "In this lab, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings.\n", + "The goal of a collaborative filtering recommender system is to generate two vectors: For each user, a 'parameter vector' that embodies the movie tastes of a user. For each movie, a feature vector of the same size which embodies some description of the movie. The dot product of the two vectors plus the bias term should produce an estimate of the rating the user might give to that movie.\n", + "\n", + "The diagram below details how these vectors are learned." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Existing ratings are provided in matrix form as shown. $Y$ contains ratings; 0.5 to 5 inclusive in 0.5 steps. 0 if the movie has not been rated. $R$ has a 1 where movies have been rated. Movies are in rows, users in columns. Each user has a parameter vector $w^{user}$ and bias. Each movie has a feature vector $x^{movie}$. These vectors are simultaneously learned by using the existing user/movie ratings as training data. One training example is shown above: $\\mathbf{w}^{(1)} \\cdot \\mathbf{x}^{(1)} + b^{(1)} = 4$. It is worth noting that the feature vector $x^{movie}$ must satisfy all the users while the user vector $w^{user}$ must satisfy all the movies. This is the source of the name of this approach - all the users collaborate to generate the rating set. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the feature vectors and parameters are learned, they can be used to predict how a user might rate an unrated movie. This is shown in the diagram above. The equation is an example of predicting a rating for user one on movie zero." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "In this exercise, you will implement the function `cofiCostFunc` that computes the collaborative filtering\n", + "objective function. After implementing the objective function, you will use a TensorFlow custom training loop to learn the parameters for collaborative filtering. The first step is to detail the data set and data structures that will be used in the lab." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6-09Hto6odYD" + }, + "source": [ + "\n", + "## 3 - Movie ratings dataset \n", + "The data set is derived from the [MovieLens \"ml-latest-small\"](https://grouplens.org/datasets/movielens/latest/) dataset. \n", + "[F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. ]\n", + "\n", + "The original dataset has 9000 movies rated by 600 users. The dataset has been reduced in size to focus on movies from the years since 2000. This dataset consists of ratings on a scale of 0.5 to 5 in 0.5 step increments. The reduced dataset has $n_u = 443$ users, and $n_m= 4778$ movies. \n", + "\n", + "Below, you will load the movie dataset into the variables $Y$ and $R$.\n", + "\n", + "The matrix $Y$ (a $n_m \\times n_u$ matrix) stores the ratings $y^{(i,j)}$. The matrix $R$ is an binary-valued indicator matrix, where $R(i,j) = 1$ if user $j$ gave a rating to movie $i$, and $R(i,j)=0$ otherwise. \n", + "\n", + "Throughout this part of the exercise, you will also be working with the\n", + "matrices, $\\mathbf{X}$, $\\mathbf{W}$ and $\\mathbf{b}$: \n", + "\n", + "$$\\mathbf{X} = \n", + "\\begin{bmatrix}\n", + "--- (\\mathbf{x}^{(0)})^T --- \\\\\n", + "--- (\\mathbf{x}^{(1)})^T --- \\\\\n", + "\\vdots \\\\\n", + "--- (\\mathbf{x}^{(n_m-1)})^T --- \\\\\n", + "\\end{bmatrix} , \\quad\n", + "\\mathbf{W} = \n", + "\\begin{bmatrix}\n", + "--- (\\mathbf{w}^{(0)})^T --- \\\\\n", + "--- (\\mathbf{w}^{(1)})^T --- \\\\\n", + "\\vdots \\\\\n", + "--- (\\mathbf{w}^{(n_u-1)})^T --- \\\\\n", + "\\end{bmatrix},\\quad\n", + "\\mathbf{ b} = \n", + "\\begin{bmatrix}\n", + " b^{(0)} \\\\\n", + " b^{(1)} \\\\\n", + "\\vdots \\\\\n", + "b^{(n_u-1)} \\\\\n", + "\\end{bmatrix}\\quad\n", + "$$ \n", + "\n", + "The $i$-th row of $\\mathbf{X}$ corresponds to the\n", + "feature vector $x^{(i)}$ for the $i$-th movie, and the $j$-th row of\n", + "$\\mathbf{W}$ corresponds to one parameter vector $\\mathbf{w}^{(j)}$, for the\n", + "$j$-th user. Both $x^{(i)}$ and $\\mathbf{w}^{(j)}$ are $n$-dimensional\n", + "vectors. For the purposes of this exercise, you will use $n=10$, and\n", + "therefore, $\\mathbf{x}^{(i)}$ and $\\mathbf{w}^{(j)}$ have 10 elements.\n", + "Correspondingly, $\\mathbf{X}$ is a\n", + "$n_m \\times 10$ matrix and $\\mathbf{W}$ is a $n_u \\times 10$ matrix.\n", + "\n", + "We will start by loading the movie ratings dataset to understand the structure of the data.\n", + "We will load $Y$ and $R$ with the movie dataset. \n", + "We'll also load $\\mathbf{X}$, $\\mathbf{W}$, and $\\mathbf{b}$ with pre-computed values. These values will be learned later in the lab, but we'll use pre-computed values to develop the cost model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Load data\n", + "X, W, b, num_movies, num_features, num_users = load_precalc_params_small()\n", + "Y, R = load_ratings_small()\n", + "\n", + "print(\"Y\", Y.shape, \"R\", R.shape)\n", + "print(\"X\", X.shape)\n", + "print(\"W\", W.shape)\n", + "print(\"b\", b.shape)\n", + "print(\"num_features\", num_features)\n", + "print(\"num_movies\", num_movies)\n", + "print(\"num_users\", num_users)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bxm1O_wbodYF" + }, + "outputs": [], + "source": [ + "# From the matrix, we can compute statistics like average rating.\n", + "tsmean = np.mean(Y[0, R[0, :].astype(bool)])\n", + "print(f\"Average rating for movie 1 : {tsmean:0.3f} / 5\" )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4 - Collaborative filtering learning algorithm \n", + "\n", + "Now, you will begin implementing the collaborative filtering learning\n", + "algorithm. You will start by implementing the objective function. \n", + "\n", + "The collaborative filtering algorithm in the setting of movie\n", + "recommendations considers a set of $n$-dimensional parameter vectors\n", + "$\\mathbf{x}^{(0)},...,\\mathbf{x}^{(n_m-1)}$, $\\mathbf{w}^{(0)},...,\\mathbf{w}^{(n_u-1)}$ and $b^{(0)},...,b^{(n_u-1)}$, where the\n", + "model predicts the rating for movie $i$ by user $j$ as\n", + "$y^{(i,j)} = \\mathbf{w}^{(j)}\\cdot \\mathbf{x}^{(i)} + b^{(i)}$ . Given a dataset that consists of\n", + "a set of ratings produced by some users on some movies, you wish to\n", + "learn the parameter vectors $\\mathbf{x}^{(0)},...,\\mathbf{x}^{(n_m-1)},\n", + "\\mathbf{w}^{(0)},...,\\mathbf{w}^{(n_u-1)}$ and $b^{(0)},...,b^{(n_u-1)}$ that produce the best fit (minimizes\n", + "the squared error).\n", + "\n", + "You will complete the code in cofiCostFunc to compute the cost\n", + "function for collaborative filtering. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bcqg0LJWodYH" + }, + "source": [ + "\n", + "\n", + "### 4.1 Collaborative filtering cost function\n", + "\n", + "The collaborative filtering cost function is given by\n", + "$$J({\\mathbf{x}^{(0)},...,\\mathbf{x}^{(n_m-1)},\\mathbf{w}^{(0)},b^{(0)},...,\\mathbf{w}^{(n_u-1)},b^{(n_u-1)}})= \\frac{1}{2}\\sum_{(i,j):r(i,j)=1}(\\mathbf{w}^{(j)} \\cdot \\mathbf{x}^{(i)} + b^{(j)} - y^{(i,j)})^2\n", + "+\\underbrace{\n", + "\\frac{\\lambda}{2}\n", + "\\sum_{j=0}^{n_u-1}\\sum_{k=0}^{n-1}(\\mathbf{w}^{(j)}_k)^2\n", + "+ \\frac{\\lambda}{2}\\sum_{i=0}^{n_m-1}\\sum_{k=0}^{n-1}(\\mathbf{x}_k^{(i)})^2\n", + "}_{regularization}\n", + "\\tag{1}$$\n", + "The first summation in (1) is \"for all $i$, $j$ where $r(i,j)$ equals $1$\" and could be written:\n", + "\n", + "$$\n", + "= \\frac{1}{2}\\sum_{j=0}^{n_u-1} \\sum_{i=0}^{n_m-1}r(i,j)*(\\mathbf{w}^{(j)} \\cdot \\mathbf{x}^{(i)} + b^{(j)} - y^{(i,j)})^2\n", + "+\\text{regularization}\n", + "$$\n", + "\n", + "You should now write cofiCostFunc (collaborative filtering cost function) to return this cost." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 1\n", + "\n", + "**For loop Implementation:** \n", + "Start by implementing the cost function using for loops.\n", + "Consider developing the cost function in two steps. First, develop the cost function without regularization. A test case that does not include regularization is provided below to test your implementation. Once that is working, add regularization and run the tests that include regularization. Note that you should be accumulating the cost for user $j$ and movie $i$ only if $R(i,j) = 1$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: cofi_cost_func\n", + "# UNQ_C1\n", + "\n", + "def cofi_cost_func(X, W, b, Y, R, lambda_):\n", + " \"\"\"\n", + " Returns the cost for the content-based filtering\n", + " Args:\n", + " X (ndarray (num_movies,num_features)): matrix of item features\n", + " W (ndarray (num_users,num_features)) : matrix of user parameters\n", + " b (ndarray (1, num_users) : vector of user parameters\n", + " Y (ndarray (num_movies,num_users) : matrix of user ratings of movies\n", + " R (ndarray (num_movies,num_users) : matrix, where R(i, j) = 1 if the i-th movies was rated by the j-th user\n", + " lambda_ (float): regularization parameter\n", + " Returns:\n", + " J (float) : Cost\n", + " \"\"\"\n", + " nm, nu = Y.shape\n", + " J = 0\n", + " ### START CODE HERE ### \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " ### END CODE HERE ### \n", + "\n", + " return J" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Public tests\n", + "from public_tests import *\n", + "test_cofi_cost_func(cofi_cost_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " You can structure the code in two for loops similar to the summation in (1). \n", + " Implement the code without regularization first. \n", + " Note that some of the elements in (1) are vectors. Use np.dot(). You can also use np.square().\n", + " Pay close attention to which elements are indexed by i and which are indexed by j. Don't forget to divide by two.\n", + " \n", + "```python \n", + " ### START CODE HERE ### \n", + " for j in range(nu):\n", + " \n", + " \n", + " for i in range(nm):\n", + " \n", + " \n", + " ### END CODE HERE ### \n", + "``` \n", + "
\n", + " Click for more hints\n", + " \n", + " Here is some more details. The code below pulls out each element from the matrix before using it. \n", + " One could also reference the matrix directly. \n", + " This code does not contain regularization.\n", + " \n", + "```python \n", + " nm,nu = Y.shape\n", + " J = 0\n", + " ### START CODE HERE ### \n", + " for j in range(nu):\n", + " w = W[j,:]\n", + " b_j = b[0,j]\n", + " for i in range(nm):\n", + " x = \n", + " y = \n", + " r =\n", + " J += \n", + " J = J/2\n", + " ### END CODE HERE ### \n", + "\n", + "```\n", + " \n", + "
\n", + " Last Resort (full non-regularized implementation)\n", + " \n", + "```python \n", + " nm,nu = Y.shape\n", + " J = 0\n", + " ### START CODE HERE ### \n", + " for j in range(nu):\n", + " w = W[j,:]\n", + " b_j = b[0,j]\n", + " for i in range(nm):\n", + " x = X[i,:]\n", + " y = Y[i,j]\n", + " r = R[i,j]\n", + " J += np.square(r * (np.dot(w,x) + b_j - y ) )\n", + " J = J/2\n", + " ### END CODE HERE ### \n", + "```\n", + " \n", + "
\n", + " regularization\n", + " Regularization just squares each element of the W array and X array and them sums all the squared elements.\n", + " You can utilize np.square() and np.sum().\n", + "\n", + "
\n", + " regularization details\n", + " \n", + "```python \n", + " J += lambda_* (np.sum(np.square(W)) + np.sum(np.square(X)))\n", + "```\n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reduce the data set size so that this runs faster\n", + "num_users_r = 4\n", + "num_movies_r = 5 \n", + "num_features_r = 3\n", + "\n", + "X_r = X[:num_movies_r, :num_features_r]\n", + "W_r = W[:num_users_r, :num_features_r]\n", + "b_r = b[0, :num_users_r].reshape(1,-1)\n", + "Y_r = Y[:num_movies_r, :num_users_r]\n", + "R_r = R[:num_movies_r, :num_users_r]\n", + "\n", + "# Evaluate cost function\n", + "J = cofi_cost_func(X_r, W_r, b_r, Y_r, R_r, 0);\n", + "print(f\"Cost: {J:0.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xGznmQ91odYL" + }, + "source": [ + "**Expected Output (lambda = 0)**: \n", + "$13.67$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate cost function with regularization \n", + "J = cofi_cost_func(X_r, W_r, b_r, Y_r, R_r, 1.5);\n", + "print(f\"Cost (with regularization): {J:0.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1xbepzUUodYP" + }, + "source": [ + "**Expected Output**:\n", + "\n", + "28.09" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Vectorized Implementation**\n", + "\n", + "It is important to create a vectorized implementation to compute $J$, since it will later be called many times during optimization. The linear algebra utilized is not the focus of this series, so the implementation is provided. If you are an expert in linear algebra, feel free to create your version without referencing the code below. \n", + "\n", + "Run the code below and verify that it produces the same results as the non-vectorized version." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cofi_cost_func_v(X, W, b, Y, R, lambda_):\n", + " \"\"\"\n", + " Returns the cost for the content-based filtering\n", + " Vectorized for speed. Uses tensorflow operations to be compatible with custom training loop.\n", + " Args:\n", + " X (ndarray (num_movies,num_features)): matrix of item features\n", + " W (ndarray (num_users,num_features)) : matrix of user parameters\n", + " b (ndarray (1, num_users) : vector of user parameters\n", + " Y (ndarray (num_movies,num_users) : matrix of user ratings of movies\n", + " R (ndarray (num_movies,num_users) : matrix, where R(i, j) = 1 if the i-th movies was rated by the j-th user\n", + " lambda_ (float): regularization parameter\n", + " Returns:\n", + " J (float) : Cost\n", + " \"\"\"\n", + " j = (tf.linalg.matmul(X, tf.transpose(W)) + b - Y)*R\n", + " J = 0.5 * tf.reduce_sum(j**2) + (lambda_/2) * (tf.reduce_sum(X**2) + tf.reduce_sum(W**2))\n", + " return J" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate cost function\n", + "J = cofi_cost_func_v(X_r, W_r, b_r, Y_r, R_r, 0);\n", + "print(f\"Cost: {J:0.2f}\")\n", + "\n", + "# Evaluate cost function with regularization \n", + "J = cofi_cost_func_v(X_r, W_r, b_r, Y_r, R_r, 1.5);\n", + "print(f\"Cost (with regularization): {J:0.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1xbepzUUodYP" + }, + "source": [ + "**Expected Output**: \n", + "Cost: 13.67 \n", + "Cost (with regularization): 28.09" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ilaeM8yWodYR" + }, + "source": [ + "\n", + "## 5 - Learning movie recommendations \n", + "------------------------------\n", + "\n", + "After you have finished implementing the collaborative filtering cost\n", + "function, you can start training your algorithm to make\n", + "movie recommendations for yourself. \n", + "\n", + "In the cell below, you can enter your own movie choices. The algorithm will then make recommendations for you! We have filled out some values according to our preferences, but after you have things working with our choices, you should change this to match your tastes.\n", + "A list of all movies in the dataset is in the file [movie list](data/small_movie_list.csv)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WJO8Jr0UodYR" + }, + "outputs": [], + "source": [ + "movieList, movieList_df = load_Movie_List_pd()\n", + "\n", + "my_ratings = np.zeros(num_movies) # Initialize my ratings\n", + "\n", + "# Check the file small_movie_list.csv for id of each movie in our dataset\n", + "# For example, Toy Story 3 (2010) has ID 2700, so to rate it \"5\", you can set\n", + "my_ratings[2700] = 5 \n", + "\n", + "#Or suppose you did not enjoy Persuasion (2007), you can set\n", + "my_ratings[2609] = 2;\n", + "\n", + "# We have selected a few movies we liked / did not like and the ratings we\n", + "# gave are as follows:\n", + "my_ratings[929] = 5 # Lord of the Rings: The Return of the King, The\n", + "my_ratings[246] = 5 # Shrek (2001)\n", + "my_ratings[2716] = 3 # Inception\n", + "my_ratings[1150] = 5 # Incredibles, The (2004)\n", + "my_ratings[382] = 2 # Amelie (Fabuleux destin d'Amélie Poulain, Le)\n", + "my_ratings[366] = 5 # Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\n", + "my_ratings[622] = 5 # Harry Potter and the Chamber of Secrets (2002)\n", + "my_ratings[988] = 3 # Eternal Sunshine of the Spotless Mind (2004)\n", + "my_ratings[2925] = 1 # Louis Theroux: Law & Disorder (2008)\n", + "my_ratings[2937] = 1 # Nothing to Declare (Rien à déclarer)\n", + "my_ratings[793] = 5 # Pirates of the Caribbean: The Curse of the Black Pearl (2003)\n", + "my_rated = [i for i in range(len(my_ratings)) if my_ratings[i] > 0]\n", + "\n", + "print('\\nNew user ratings:\\n')\n", + "for i in range(len(my_ratings)):\n", + " if my_ratings[i] > 0 :\n", + " print(f'Rated {my_ratings[i]} for {movieList_df.loc[i,\"title\"]}');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's add these reviews to $Y$ and $R$ and normalize the ratings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reload ratings and add new ratings\n", + "Y, R = load_ratings_small()\n", + "Y = np.c_[my_ratings, Y]\n", + "R = np.c_[(my_ratings != 0).astype(int), R]\n", + "\n", + "# Normalize the Dataset\n", + "Ynorm, Ymean = normalizeRatings(Y, R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's prepare to train the model. Initialize the parameters and select the Adam optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Useful Values\n", + "num_movies, num_users = Y.shape\n", + "num_features = 100\n", + "\n", + "# Set Initial Parameters (W, X), use tf.Variable to track these variables\n", + "tf.random.set_seed(1234) # for consistent results\n", + "W = tf.Variable(tf.random.normal((num_users, num_features),dtype=tf.float64), name='W')\n", + "X = tf.Variable(tf.random.normal((num_movies, num_features),dtype=tf.float64), name='X')\n", + "b = tf.Variable(tf.random.normal((1, num_users), dtype=tf.float64), name='b')\n", + "\n", + "# Instantiate an optimizer.\n", + "optimizer = keras.optimizers.Adam(learning_rate=1e-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now train the collaborative filtering model. This will learn the parameters $\\mathbf{X}$, $\\mathbf{W}$, and $\\mathbf{b}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The operations involved in learning $w$, $b$, and $x$ simultaneously do not fall into the typical 'layers' offered in the TensorFlow neural network package. Consequently, the flow used in Course 2: Model, Compile(), Fit(), Predict(), are not directly applicable. Instead, we can use a custom training loop.\n", + "\n", + "Recall from earlier labs the steps of gradient descent.\n", + "- repeat until convergence:\n", + " - compute forward pass\n", + " - compute the derivatives of the loss relative to parameters\n", + " - update the parameters using the learning rate and the computed derivatives \n", + " \n", + "TensorFlow has the marvelous capability of calculating the derivatives for you. This is shown below. Within the `tf.GradientTape()` section, operations on Tensorflow Variables are tracked. When `tape.gradient()` is later called, it will return the gradient of the loss relative to the tracked variables. The gradients can then be applied to the parameters using an optimizer. \n", + "This is a very brief introduction to a useful feature of TensorFlow and other machine learning frameworks. Further information can be found by investigating \"custom training loops\" within the framework of interest.\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iterations = 200\n", + "lambda_ = 1\n", + "for iter in range(iterations):\n", + " # Use TensorFlow’s GradientTape\n", + " # to record the operations used to compute the cost \n", + " with tf.GradientTape() as tape:\n", + "\n", + " # Compute the cost (forward pass included in cost)\n", + " cost_value = cofi_cost_func_v(X, W, b, Ynorm, R, lambda_)\n", + "\n", + " # Use the gradient tape to automatically retrieve\n", + " # the gradients of the trainable variables with respect to the loss\n", + " grads = tape.gradient( cost_value, [X,W,b] )\n", + "\n", + " # Run one step of gradient descent by updating\n", + " # the value of the variables to minimize the loss.\n", + " optimizer.apply_gradients( zip(grads, [X,W,b]) )\n", + "\n", + " # Log periodically.\n", + " if iter % 20 == 0:\n", + " print(f\"Training loss at iteration {iter}: {cost_value:0.1f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SSzUL7eQodYS" + }, + "source": [ + "\n", + "## 6 - Recommendations\n", + "Below, we compute the ratings for all the movies and users and display the movies that are recommended. These are based on the movies and ratings entered as `my_ratings[]` above. To predict the rating of movie $i$ for user $j$, you compute $\\mathbf{w}^{(j)} \\cdot \\mathbf{x}^{(i)} + b^{(j)}$. This can be computed for all ratings using matrix multiplication." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ns266wKtodYT" + }, + "outputs": [], + "source": [ + "# Make a prediction using trained weights and biases\n", + "p = np.matmul(X.numpy(), np.transpose(W.numpy())) + b.numpy()\n", + "\n", + "#restore the mean\n", + "pm = p + Ymean\n", + "\n", + "my_predictions = pm[:,0]\n", + "\n", + "# sort predictions\n", + "ix = tf.argsort(my_predictions, direction='DESCENDING')\n", + "\n", + "for i in range(17):\n", + " j = ix[i]\n", + " if j not in my_rated:\n", + " print(f'Predicting rating {my_predictions[j]:0.2f} for movie {movieList[j]}')\n", + "\n", + "print('\\n\\nOriginal vs Predicted ratings:\\n')\n", + "for i in range(len(my_ratings)):\n", + " if my_ratings[i] > 0:\n", + " print(f'Original {my_ratings[i]}, Predicted {my_predictions[i]:0.2f} for {movieList[i]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In practice, additional information can be utilized to enhance our predictions. Above, the predicted ratings for the first few hundred movies lie in a small range. We can augment the above by selecting from those top movies, movies that have high average ratings and movies with more than 20 ratings. This section uses a [Pandas](https://pandas.pydata.org/) data frame which has many handy sorting features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filter=(movieList_df[\"number of ratings\"] > 20)\n", + "movieList_df[\"pred\"] = my_predictions\n", + "movieList_df = movieList_df.reindex(columns=[\"pred\", \"mean rating\", \"number of ratings\", \"title\"])\n", + "movieList_df.loc[ix[:300]].loc[filter].sort_values(\"mean rating\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7 - Congratulations! \n", + "You have implemented a useful recommender system!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/__pycache__/public_tests.cpython-37.pyc b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/__pycache__/public_tests.cpython-37.pyc new file mode 100644 index 00000000..ab507205 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/__pycache__/public_tests.cpython-37.pyc differ diff --git a/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/__pycache__/recsys_utils.cpython-37.pyc b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/__pycache__/recsys_utils.cpython-37.pyc new file mode 100644 index 00000000..3c3acee1 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/__pycache__/recsys_utils.cpython-37.pyc differ diff --git a/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/data/small_movie_list.csv b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/data/small_movie_list.csv new file mode 100644 index 00000000..3d8078e3 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/data/small_movie_list.csv @@ -0,0 +1,4779 @@ +,mean rating,number of ratings,title +0,3.4,5,"Yards, The (2000)" +1,3.25,6,Next Friday (2000) +2,2.0,4,Supernova (2000) +3,2.0,4,Down to You (2000) +4,2.6724137931034484,29,Scream 3 (2000) +5,4.22093023255814,43,"Boondock Saints, The (2000)" +6,1.0,1,Gun Shy (2000) +7,3.0625,32,"Beach, The (2000)" +8,2.3,5,Snow Day (2000) +9,3.1666666666666665,3,"Tigger Movie, The (2000)" +10,3.7142857142857144,21,Boiler Room (2000) +11,2.6666666666666665,3,Hanging Up (2000) +12,3.5641025641025643,39,Pitch Black (2000) +13,3.5238095238095237,42,"Whole Nine Yards, The (2000)" +14,5.0,1,Black Tar Heroin: The Dark End of the Street (2000) +15,2.4615384615384617,13,Reindeer Games (2000) +16,3.659090909090909,22,Wonder Boys (2000) +17,4.0,1,Chain of Fools (2000) +18,1.3333333333333333,3,Drowning Mona (2000) +19,2.1666666666666665,3,"Next Best Thing, The (2000)" +20,3.1666666666666665,3,What Planet Are You From? (2000) +21,4.5,1,"Closer You Get, The (2000)" +22,2.6904761904761907,21,Mission to Mars (2000) +23,3.5285714285714285,70,Erin Brockovich (2000) +24,2.9285714285714284,35,Final Destination (2000) +25,3.0833333333333335,18,Romeo Must Die (2000) +26,3.0,2,Here on Earth (2000) +27,2.25,2,Whatever It Takes (2000) +28,3.6666666666666665,75,High Fidelity (2000) +29,3.1153846153846154,13,"Road to El Dorado, The (2000)" +30,2.75,10,"Skulls, The (2000)" +31,3.4166666666666665,36,Frequency (2000) +32,2.25,6,Ready to Rumble (2000) +33,3.5625,8,Return to Me (2000) +34,3.0714285714285716,7,Rules of Engagement (2000) +35,3.0,1,Joe Gould's Secret (2000) +36,3.611111111111111,9,Me Myself I (2000) +37,3.3,25,28 Days (2000) +38,3.788135593220339,59,American Psycho (2000) +39,3.4523809523809526,21,Keeping the Faith (2000) +40,3.3333333333333335,3,Where the Money Is (2000) +41,3.25,2,"Filth and the Fury, The (2000)" +42,2.4,5,Gossip (2000) +43,3.0,3,Love and Basketball (2000) +44,3.4242424242424243,33,U-571 (2000) +45,2.5,3,"Crow: Salvation, The (2000)" +46,1.625,12,"Flintstones in Viva Rock Vegas, The (2000)" +47,3.5,7,Where the Heart Is (2000) +48,2.875,8,"Big Kahuna, The (2000)" +49,5.0,1,Bossa Nova (2000) +50,3.0,2,Time Code (2000) +51,3.9382352941176473,170,Gladiator (2000) +52,3.5,2,Up at the Villa (2000) +53,1.6578947368421053,19,Battlefield Earth (2000) +54,3.25,6,Center Stage (2000) +55,1.0,1,Screwed (2000) +56,2.0,2,Whipped (2000) +57,3.0,3,Hamlet (2000) +58,3.107142857142857,14,Dinosaur (2000) +59,3.3,10,Loser (2000) +60,3.0853658536585367,41,Road Trip (2000) +61,3.5416666666666665,12,Small Time Crooks (2000) +62,2.7142857142857144,77,Mission: Impossible II (2000) +63,3.1511627906976742,43,Shanghai Noon (2000) +64,2.176470588235294,17,Big Momma's House (2000) +65,3.221311475409836,61,Gone in 60 Seconds (2000) +66,3.0,2,Love's Labour's Lost (2000) +67,2.0,3,Boys and Girls (2000) +68,2.6578947368421053,19,Shaft (2000) +69,3.413793103448276,29,Titan A.E. (2000) +70,3.551282051282051,78,Chicken Run (2000) +71,3.0869565217391304,46,"Me, Myself & Irene (2000)" +72,3.448529411764706,68,"Patriot, The (2000)" +73,2.2222222222222223,9,"Adventures of Rocky and Bullwinkle, The (2000)" +74,3.1704545454545454,44,"Perfect Storm, The (2000)" +75,4.0,1,"Golden Bowl, The (2000)" +76,2.590909090909091,11,"Kid, The (2000)" +77,2.92,50,Scary Movie (2000) +78,4.0,2,Groove (2000) +79,3.699248120300752,133,X-Men (2000) +80,2.5,2,Chuck & Buck (2000) +81,2.6666666666666665,3,"In Crowd, The (2000)" +82,3.2,25,What Lies Beneath (2000) +83,1.5833333333333333,6,Pokémon the Movie 2000 (2000) +84,2.28125,16,Nutty Professor II: The Klumps (2000) +85,2.409090909090909,11,Autumn in New York (2000) +86,2.7413793103448274,29,Coyote Ugly (2000) +87,2.2948717948717947,39,Hollow Man (2000) +88,2.891304347826087,23,Space Cowboys (2000) +89,1.5,2,Psycho Beach Party (2000) +90,3.909090909090909,11,Saving Grace (2000) +91,5.0,1,I'm the One That I Want (2000) +92,3.3846153846153846,13,"Tao of Steve, The (2000)" +93,4.0,1,Bless the Child (2000) +94,3.5,5,Cecil B. DeMented (2000) +95,3.75,2,"Eyes of Tammy Faye, The (2000)" +96,2.6842105263157894,19,"Replacements, The (2000)" +97,3.75,2,About Adam (2000) +98,3.0444444444444443,45,"Cell, The (2000)" +99,3.25,4,"Original Kings of Comedy, The (2000)" +100,2.0,2,"Art of War, The (2000)" +101,2.6911764705882355,34,Bring It On (2000) +102,1.5,3,"Crew, The (2000)" +103,4.0,1,Skipped Parts (2000) +104,2.25,6,Highlander: Endgame (Highlander IV) (2000) +105,3.3421052631578947,19,Nurse Betty (2000) +106,1.5,1,"Watcher, The (2000)" +107,3.125,8,"Way of the Gun, The (2000)" +108,3.8674698795180724,83,Almost Famous (2000) +109,2.5,2,Bait (2000) +110,1.0,1,Circus (2000) +111,4.0,1,Crime and Punishment in Suburbia (2000) +112,3.25,2,Duets (2000) +113,2.5,2,Under Suspicion (2000) +114,1.5,3,Urban Legends: Final Cut (2000) +115,2.0,4,Woman on Top (2000) +116,3.975,20,Dancer in the Dark (2000) +117,3.688679245283019,53,Best in Show (2000) +118,2.5,2,Beautiful (2000) +119,4.0,1,"Broken Hearts Club, The (2000)" +120,3.6666666666666665,6,Girlfight (2000) +121,3.7439024390243905,41,Remember the Titans (2000) +122,2.9375,8,Bamboozled (2000) +123,3.0,1,Digimon: The Movie (2000) +124,2.5,5,Get Carter (2000) +125,3.4175824175824174,91,Meet the Parents (2000) +126,3.921875,96,Requiem for a Dream (2000) +127,3.5625,8,Tigerland (2000) +128,5.0,1,Two Family House (2000) +129,3.0714285714285716,7,"Contender, The (2000)" +130,2.125,4,Dr. T and the Women (2000) +131,1.5833333333333333,6,"Ladies Man, The (2000)" +132,3.8513513513513513,37,Billy Elliot (2000) +133,3.074074074074074,27,Bedazzled (2000) +134,3.4423076923076925,26,Pay It Forward (2000) +135,1.125,4,Book of Shadows: Blair Witch 2 (2000) +136,3.0,1,"Little Vampire, The (2000)" +137,2.7222222222222223,72,Charlie's Angels (2000) +138,3.066666666666667,15,"Legend of Bagger Vance, The (2000)" +139,2.5,24,Little Nicky (2000) +140,3.392857142857143,14,Men of Honor (2000) +141,2.3461538461538463,13,Red Planet (2000) +142,4.166666666666667,9,You Can Count on Me (2000) +143,2.8,20,"6th Day, The (2000)" +144,2.4166666666666665,6,Bounce (2000) +145,3.0454545454545454,33,How the Grinch Stole Christmas (a.k.a. The Grinch) (2000) +146,2.25,4,Rugrats in Paris: The Movie (2000) +147,2.7777777777777777,9,102 Dalmatians (2000) +148,3.7,5,Malèna (2000) +149,3.0,6,Quills (2000) +150,3.4794520547945207,73,Unbreakable (2000) +151,3.8363636363636364,110,"Crouching Tiger, Hidden Dragon (Wo hu cang long) (2000)" +152,1.8333333333333333,12,Dungeons & Dragons (2000) +153,3.0416666666666665,12,Proof of Life (2000) +154,2.607142857142857,14,Vertical Limit (2000) +155,4.155913978494624,93,Snatch (2000) +156,3.521276595744681,47,Chocolat (2000) +157,2.727272727272727,33,"Dude, Where's My Car? 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MIB 2) (2002) +524,3.6666666666666665,3,"Powerpuff Girls, The (2002)" +525,2.5,1,Me Without You (2001) +526,2.5,2,"Crocodile Hunter: Collision Course, The (2002)" +527,2.7222222222222223,18,Reign of Fire (2002) +528,3.520408163265306,49,Road to Perdition (2002) +529,3.0,1,All About Lily Chou-Chou (Riri Shushu no subete) (2001) +530,4.0,4,My Wife is an Actress (Ma Femme est une Actrice) (2001) +531,2.0,3,Halloween: Resurrection (Halloween 8) (2002) +532,3.875,4,Sex and Lucia (Lucía y el sexo) (2001) +533,2.75,8,Eight Legged Freaks (2002) +534,3.5,6,K-19: The Widowmaker (2002) +535,2.6,5,Stuart Little 2 (2002) +536,2.8461538461538463,65,Austin Powers in Goldmember (2002) +537,3.8333333333333335,3,"Kid Stays in the Picture, The (2002)" +538,3.3333333333333335,3,Tadpole (2002) +539,2.5,2,Who Is Cletis Tout? 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Amy's Orgasm) (2001) +560,5.0,1,Satin Rouge (2002) +561,1.5,3,FearDotCom (a.k.a. Fear.com) (a.k.a. Fear Dot Com) (2002) +562,2.5,1,Snipes (2001) +563,2.8333333333333335,3,City by the Sea (2002) +564,2.0,2,Swimfan (2002) +565,2.7142857142857144,7,Barbershop (2002) +566,2.2857142857142856,7,Stealing Harvard (2002) +567,3.519230769230769,26,"Transporter, The (2002)" +568,2.0,1,Alias Betty (Betty Fisher et autres histoires) (2001) +569,3.6666666666666665,12,Igby Goes Down (2002) +570,5.0,1,"Son of the Bride (Hijo de la novia, El) (2001)" +571,3.8636363636363638,11,"Das Experiment (Experiment, The) (2001)" +572,2.5,2,Ballistic: Ecks vs. Sever (2002) +573,2.0,3,"Banger Sisters, The (2002)" +574,2.75,2,"Four Feathers, The (2002)" +575,2.0,1,Trapped (2002) +576,3.8333333333333335,3,8 Women (2002) +577,4.5,1,"His Secret Life (a.k.a. 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He Loves Me Not (À la folie... pas du tout) (2002) +694,3.0,3,Dark Blue (2003) +695,3.0,1,Gods and Generals (2003) +696,3.8125,8,"Life of David Gale, The (2003)" +697,3.5128205128205128,39,Old School (2003) +698,5.0,1,Open Hearts (Elsker dig for evigt) (2002) +699,3.8333333333333335,3,Poolhall Junkies (2002) +700,4.0,2,Stone Reader (2002) +701,3.5,3,Cradle 2 the Grave (2003) +702,3.3333333333333335,3,Spider (2002) +703,3.5,1,Late Marriage (Hatuna Meuheret) (2001) +704,3.0,1,Volcano High (Whasango) (2001) +705,1.9166666666666667,6,Bringing Down the House (2003) +706,2.642857142857143,7,Tears of the Sun (2003) +707,3.888888888888889,9,Irreversible (Irréversible) (2002) +708,3.0,3,Laurel Canyon (2002) +709,3.75,2,Nowhere in Africa (Nirgendwo in Afrika) (2001) +710,3.0,2,"Safety of Objects, The (2001)" +711,3.3,40,Bend It Like Beckham (2002) +712,3.0,3,"Hunted, The (2003)" +713,2.5,4,Willard (2003) +714,3.0,1,Prozac Nation (2001) +715,3.25,2,Spun (2001) +716,2.375,4,Boat Trip (2003) +717,2.642857142857143,7,Dreamcatcher (2003) +718,2.0,1,Piglet's Big Movie (2003) +719,2.375,4,View from the Top (2003) +720,3.75,2,Basic (2003) +721,2.375,8,"Core, The (2003)" +722,2.25,2,Head of State (2003) +723,3.0625,8,What a Girl Wants (2003) +724,2.0,1,Assassination Tango (2002) +725,3.75,2,Raising Victor Vargas (2002) +726,4.25,2,Stevie (2002) +727,4.0,1,"Good Thief, The (2002)" +728,2.25,2,"Man Apart, A (2003)" +729,3.140625,32,Phone Booth (2002) +730,3.9,20,Cowboy Bebop: The Movie (Cowboy Bebop: Tengoku no Tobira) (2001) +731,4.0,1,Levity (2003) +732,3.875,4,"Man Without a Past, The (Mies vailla menneisyyttä) (2002)" +733,3.0588235294117645,34,Anger Management (2003) +734,3.875,4,Better Luck Tomorrow (2002) +735,3.5,1,Ghosts of the Abyss (2003) +736,2.7142857142857144,7,House of 1000 Corpses (2003) +737,3.5,3,Lilya 4-Ever (Lilja 4-ever) (2002) +738,2.409090909090909,11,Bulletproof Monk (2003) +739,2.0,1,Chasing Papi (a.k.a. Papi Chulo) (2003) +740,3.75,14,"Mighty Wind, A (2003)" +741,3.425,20,Holes (2003) +742,2.6666666666666665,3,Malibu's Most Wanted (2003) +743,3.5,9,"Winged Migration (Peuple migrateur, Le) (2001)" +744,4.5,3,Flickering Lights (Blinkende lygter) (2000) +745,4.166666666666667,3,I Am Trying to Break Your Heart (2002) +746,2.8333333333333335,6,Confidence (2003) +747,3.6818181818181817,22,Identity (2003) +748,2.5,2,It Runs in the Family (2003) +749,3.5,3,"Decade Under the Influence, A (2003)" +750,3.0,1,Manic (2001) +751,3.0,2,People I Know (2002) +752,3.923076923076923,13,Spellbound (2002) +753,3.0,4,"Lizzie McGuire Movie, The (2003)" +754,3.723684210526316,76,X2: X-Men United (2003) +755,3.5,3,Blue Car (2002) +756,3.8333333333333335,3,"Dancer Upstairs, The (2002)" +757,3.0,1,Marooned in Iraq (Gomgashtei dar Aragh) (2002) +758,3.5,4,Owning Mahowny (2003) +759,2.3333333333333335,6,Daddy Day Care (2003) +760,3.875,4,"Man on the Train (Homme du train, L') (2002)" +761,3.25,4,"Shape of Things, The (2003)" +762,2.0,1,"Trip, The (2002)" +763,3.5,1,101 Reykjavik (101 Reykjavík) (2000) +764,3.3541666666666665,96,"Matrix Reloaded, The (2003)" +765,2.9,10,Down with Love (2003) +766,4.0,4,Cinemania (2002) +767,4.0,5,"Spanish Apartment, The (L'auberge espagnole) (2002)" +768,0.5,1,Pokémon Heroes (2003) +769,3.316901408450704,71,Bruce Almighty (2003) +770,2.1666666666666665,3,"In-Laws, The (2003)" +771,4.0,4,Gigantic (A Tale of Two Johns) (2002) +772,3.5,2,Respiro (2002) +773,3.9609929078014185,141,Finding Nemo (2003) +774,3.6186440677966103,59,"Italian Job, The (2003)" +775,3.0,2,Wrong Turn (2003) +776,3.9285714285714284,7,Capturing the Friedmans (2003) +777,3.0,1,Together (Han ni Zai Yiki) (2002) +778,2.6052631578947367,19,"2 Fast 2 Furious (Fast and the Furious 2, The) (2003)" +779,3.75,14,Whale Rider (2002) +780,3.5,1,Murder on a Sunday Morning (Un coupable idéal) (2001) +781,1.9545454545454546,11,Dumb and Dumberer: When Harry Met Lloyd (2003) +782,0.8333333333333334,3,From Justin to Kelly (2003) +783,2.9285714285714284,7,Hollywood Homicide (2003) +784,2.0,1,Alex and Emma (2003) +785,3.9741379310344827,58,28 Days Later (2002) +786,2.3703703703703702,27,Charlie's Angels: Full Throttle (2003) +787,3.5,1,Fulltime Killer (Chuen jik sat sau) (2001) +788,2.5606060606060606,33,Hulk (2003) +789,2.409090909090909,11,"Legally Blonde 2: Red, White & Blonde (2003)" +790,3.6666666666666665,6,Sinbad: Legend of the Seven Seas (2003) +791,3.0444444444444443,45,Terminator 3: Rise of the Machines (2003) +792,3.6,5,Swimming Pool (2003) +793,3.778523489932886,149,Pirates of the Caribbean: The Curse of the Black Pearl (2003) +794,2.625,32,"League of Extraordinary Gentlemen, The (a.k.a. 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Chavez: Inside the Coup) (2003)" +897,3.6923076923076925,39,Elf (2003) +898,4.0,1,Billabong Odyssey (2003) +899,3.788135593220339,59,Love Actually (2003) +900,4.25,2,My Architect: A Son's Journey (2003) +901,2.75,2,Looney Tunes: Back in Action (2003) +902,3.638888888888889,36,Master and Commander: The Far Side of the World (2003) +903,4.0,1,Tupac: Resurrection (2003) +904,3.5,1,"Big Empty, The (2003)" +905,3.6666666666666665,3,"Missing, The (2003)" +906,2.1,5,"Cat in the Hat, The (2003)" +907,3.111111111111111,9,Gothika (2003) +908,3.3,25,21 Grams (2003) +909,4.0,3,"Barbarian Invasions, The (Les invasions barbares) (2003)" +910,3.3653846153846154,26,Bad Santa (2003) +911,2.5,10,"Haunted Mansion, The (2003)" +912,2.0,3,Timeline (2003) +913,3.5,1,OT: Our Town (2002) +914,4.25,2,Devil's Playground (2002) +915,3.5,1,Journeys with George (2002) +916,3.933333333333333,30,Battle Royale (Batoru rowaiaru) (2000) +917,4.0,1,Things You Can Tell Just by Looking at Her (2000) +918,3.9210526315789473,38,Hero (Ying xiong) (2002) +919,5.0,1,Rivers and Tides (2001) +920,3.55,10,"Cooler, The (2003)" +921,3.857142857142857,7,In America (2002) +922,4.0,1,My Flesh and Blood (2003) +923,3.0,3,Honey (2003) +924,3.903225806451613,62,"Last Samurai, The (2003)" +925,3.8333333333333335,69,Big Fish (2003) +926,3.6470588235294117,17,Something's Gotta Give (2003) +927,2.8,10,Stuck on You (2003) +928,3.375,12,Girl with a Pearl Earring (2003) +929,4.118918918918919,185,"Lord of the Rings: The Return of the King, The (2003)" +930,2.8,10,Mona Lisa Smile (2003) +931,2.625,8,Calendar Girls (2003) +932,4.3076923076923075,13,"Fog of War: Eleven Lessons from the Life of Robert S. McNamara, The (2003)" +933,3.7777777777777777,9,House of Sand and Fog (2003) +934,3.5294117647058822,17,Monster (2003) +935,1.9375,8,Cheaper by the Dozen (2003) +936,3.2916666666666665,12,Cold Mountain (2003) +937,2.911764705882353,17,Paycheck (2003) +938,3.0833333333333335,6,Peter Pan (2003) +939,3.0,1,"Company, The (2003)" +940,3.0,1,Japanese Story (2003) +941,3.9375,8,Chasing Liberty (2004) +942,3.5,2,Aileen: Life and Death of a Serial Killer (2003) +943,3.0416666666666665,24,Along Came Polly (2004) +944,2.25,2,Torque (2004) +945,3.5,1,Crimson Gold (Talaye sorgh) (2003) +946,3.5,1,Osama (2003) +947,3.0,1,Beyond Re-Animator (2003) +948,3.4166666666666665,6,Ichi the Killer (Koroshiya 1) (2001) +949,3.5,1,"Suriyothai (a.k.a. Legend of Suriyothai, The) (2001)" +950,3.630434782608696,46,The Butterfly Effect (2004) +951,2.8125,8,Win a Date with Tad Hamilton! 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(2004) +1134,3.5625,8,Friday Night Lights (2004) +1135,4.0,1,Tarnation (2003) +1136,3.6666666666666665,3,"Final Cut, The (2004)" +1137,1.75,2,Being Julia (2004) +1138,2.75,2,Surviving Christmas (2004) +1139,2.3,10,"Grudge, The (2004)" +1140,2.3333333333333335,3,Alfie (2004) +1141,3.7435897435897436,39,Sideways (2004) +1142,3.986842105263158,38,The Machinist (2004) +1143,3.0,4,Vera Drake (2004) +1144,3.5,1,Falling Angels (2003) +1145,4.0,1,Lightning in a Bottle (2004) +1146,3.0,1,Undertow (2004) +1147,3.1818181818181817,33,Saw (2004) +1148,3.6842105263157894,19,Ray (2004) +1149,2.5,3,Birth (2004) +1150,3.836,125,"Incredibles, The (2004)" +1151,3.0,1,Callas Forever (2002) +1152,3.1363636363636362,11,"Polar Express, The (2004)" +1153,3.125,8,Kinsey (2004) +1154,1.75,4,Seed of Chucky (Child's Play 5) (2004) +1155,3.5833333333333335,6,After the Sunset (2004) +1156,3.025,20,Bridget Jones: The Edge of Reason (2004) +1157,3.609375,32,Finding Neverland (2004) +1158,3.263157894736842,38,National Treasure (2004) +1159,3.75,8,Bad Education (La mala educación) (2004) +1160,3.0,2,"SpongeBob SquarePants Movie, The (2004)" +1161,2.2,10,Alexander (2004) +1162,2.5,2,Christmas with the Kranks (2004) +1163,4.0,1,Guerrilla: The Taking of Patty Hearst (2004) +1164,3.71875,16,Closer (2004) +1165,3.0,3,I Am David (2003) +1166,3.52,25,House of Flying Daggers (Shi mian mai fu) (2004) +1167,3.2906976744186047,43,Ocean's Twelve (2004) +1168,2.857142857142857,14,Blade: Trinity (2004) +1169,3.0,1,Bush's Brain (2004) +1170,4.1,5,Love Me If You Dare (Jeux d'enfants) (2003) +1171,4.333333333333333,3,Control Room (2004) +1172,4.0,1,Dark Portals: The Chronicles of Vidocq (Vidocq) (2001) +1173,4.0,1,In July (Im Juli) (2000) +1174,3.875,4,Taxi 2 (2000) +1175,4.0,1,If These Walls Could Talk 2 (2000) +1176,2.75,2,"10th Kingdom, The (2000)" +1177,4.071428571428571,7,2046 (2004) +1178,1.5,1,Bartleby (2001) +1179,3.5,3,Batman Beyond: Return of the Joker (2000) +1180,5.0,1,"Nine Lives of Tomas Katz, The (2000)" +1181,4.5,1,Monday (2000) +1182,4.0,1,Paradise Lost 2: Revelations (2000) +1183,4.0,3,Asterix & Obelix: Mission Cleopatra (Astérix & Obélix: Mission Cléopâtre) (2002) +1184,4.0,1,Daria: Is It Fall Yet? (2000) +1185,4.5,1,Late Night Shopping (2001) +1186,5.0,1,61* (2001) +1187,4.25,4,Joint Security Area (Gongdong gyeongbi guyeok JSA) (2000) +1188,3.3333333333333335,3,Ripley's Game (2002) +1189,3.75,2,Jalla! Jalla! (2000) +1190,2.0,1,Teknolust (2002) +1191,2.5,1,"Accidental Spy, The (Dak miu mai shing) (2001)" +1192,1.5,3,Darkness (2002) +1193,3.25,2,Blood: The Last Vampire (2000) +1194,2.5,1,Blueberry (2004) +1195,2.5,1,American Psycho II: All American Girl (2002) +1196,3.0,3,Ali G Indahouse (2002) +1197,4.5,1,Dead or Alive 2: Tôbôsha (2000) +1198,3.75,2,Cube 2: Hypercube (2002) +1199,3.0,1,Pulse (Kairo) (2001) +1200,3.0,1,Dog Days (Hundstage) (2001) +1201,5.0,1,My Sassy Girl (Yeopgijeogin geunyeo) (2001) +1202,3.5,1,Nothing (2003) +1203,2.5,1,Undertaking Betty (Plots with a View) (2002) +1204,1.5,1,Dead or Alive: Final (2002) +1205,3.75,2,Fubar (2002) +1206,4.333333333333333,3,"Happiness of the Katakuris, The (Katakuri-ke no kôfuku) (2001)" +1207,3.5,2,Dead End (2003) +1208,3.8,5,Sympathy for Mr. Vengeance (Boksuneun naui geot) (2002) +1209,0.5,1,Jesus Christ Vampire Hunter (2001) +1210,3.125,4,Suicide Club (Jisatsu saakuru) (2001) +1211,3.8157894736842106,19,Battlestar Galactica (2003) +1212,2.75,2,"Sound of Thunder, A (2005)" +1213,4.5,2,"Lion King 1½, The (2004)" +1214,4.5,1,Oasis (2002) +1215,3.5,1,Remember Me (Ricordati di me) (2003) +1216,3.7,20,"Animatrix, The (2003)" +1217,3.25,2,"Brown Bunny, The (2003)" +1218,3.0,1,Ju-on: The Curse (2000) +1219,3.75,4,11:14 (2003) +1220,2.0,1,Tremors 4: The Legend Begins (2004) +1221,1.75,2,Bring It On Again (2004) +1222,2.5,1,"Crimson Rivers 2: Angels of the Apocalypse (Rivières pourpres II - Les anges de l'apocalypse, Les) (2004)" +1223,3.5,1,And Starring Pancho Villa as Himself (2003) +1224,3.0,1,Nicotina (2003) +1225,5.0,1,Battle Royale 2: Requiem (Batoru rowaiaru II: Chinkonka) (2003) +1226,1.5,1,In Hell (2003) +1227,3.16,25,Lemony Snicket's A Series of Unfortunate Events (2004) +1228,4.0,1,Helen of Troy (2003) +1229,4.5,1,"Green Butchers, The (Grønne slagtere, De) (2003)" +1230,3.6666666666666665,6,"Very Long Engagement, A (Un long dimanche de fiançailles) (2004)" +1231,4.166666666666667,3,Last Life in the Universe (Ruang rak noi nid mahasan) (2003) +1232,2.1666666666666665,3,Ghost in the Shell 2: Innocence (a.k.a. Innocence) (Inosensu) (2004) +1233,3.75,8,"Cat Returns, The (Neko no ongaeshi) (2002)" +1234,3.8333333333333335,6,"Twilight Samurai, The (Tasogare Seibei) (2002)" +1235,4.5,1,"Facing Windows (Finestra di fronte, La) (2003)" +1236,3.0,1,Ginger Snaps: Unleashed (2004) +1237,5.0,1,'Salem's Lot (2004) +1238,2.5,1,Comic Book: The Movie (2004) +1239,3.75,2,Intimate Strangers (Confidences trop intimes) (2004) +1240,3.0,1,Down to the Bone (2004) +1241,2.5833333333333335,6,Ju-on: The Grudge (2002) +1242,4.089743589743589,39,Old Boy (2003) +1243,4.5,1,Red Lights (Feux rouges) (2004) +1244,3.0,1,Ginger Snaps Back: The Beginning (2004) +1245,1.5,1,One Missed Call (Chakushin ari) (2003) +1246,3.0,8,"Jacket, The (2005)" +1247,3.5,4,Millions (2004) +1248,1.5,2,Starship Troopers 2: Hero of the Federation (2004) +1249,4.0,12,Ong-Bak: The Thai Warrior (Ong Bak) (2003) +1250,3.0,1,Infernal Affairs 2 (Mou gaan dou II) (2003) +1251,4.0,8,"Sea Inside, The (Mar adentro) (2004)" +1252,3.3636363636363638,11,Spanglish (2004) +1253,3.909090909090909,11,"Chorus, The (Choristes, Les) (2004)" +1254,2.5,1,Saints and Soldiers (2003) +1255,4.0,2,"Story of the Weeping Camel, The (Geschichte vom weinenden Kamel, Die) (2003)" +1256,3.25,6,"Interpreter, The (2005)" +1257,2.7,5,Open Water (2003) +1258,4.0,1,Touch of Pink (2004) +1259,3.5,1,Slasher (2004) +1260,2.5,1,"Bobby Jones, Stroke of Genius (2004)" +1261,3.8157894736842106,19,Layer Cake (2004) +1262,4.333333333333333,3,"Return, The (Vozvrashcheniye) (2003)" +1263,2.875,4,Flight of the Phoenix (2004) +1264,3.75,2,Mean Creek (2004) +1265,2.3,5,"Ring Two, The (2005)" +1266,3.9,10,"Corporation, The (2003)" +1267,3.75,2,"Yes Men, The (2003)" +1268,2.75,2,Azumi (2003) +1269,3.5,1,In My Father's Den (2004) +1270,4.5,1,Tae Guk Gi: The Brotherhood of War (Taegukgi hwinalrimyeo) (2004) +1271,3.1666666666666665,3,Metallica: Some Kind of Monster (2004) +1272,3.875,4,Born into Brothels (2004) +1273,4.125,4,DiG! (2004) +1274,4.0,4,Riding Giants (2004) +1275,2.75,2,What the #$*! Do We Know!? (a.k.a. What the Bleep Do We Know!?) (2004) +1276,3.6136363636363638,22,"Scanner Darkly, A (2006)" +1277,3.5,1,Casshern (2004) +1278,3.3,5,Outfoxed: Rupert Murdoch's War on Journalism (2004) +1279,3.8461538461538463,52,Million Dollar Baby (2004) +1280,4.75,2,Gozu (Gokudô kyôfu dai-gekijô: Gozu) (2003) +1281,3.7916666666666665,48,Hotel Rwanda (2004) +1282,3.0,61,Charlie and the Chocolate Factory (2005) +1283,4.5,3,3-Iron (Bin-jip) (2004) +1284,3.4864864864864864,37,"Life Aquatic with Steve Zissou, The (2004)" +1285,3.5285714285714285,35,"Aviator, The (2004)" +1286,3.1666666666666665,12,"Phantom of the Opera, The (2004)" +1287,2.0,1,Beyond the Sea (2004) +1288,3.8333333333333335,3,"Woodsman, The (2004)" +1289,3.4545454545454546,11,In Good Company (2004) +1290,3.106060606060606,33,Meet the Fockers (2004) +1291,3.5,2,"Assassination of Richard Nixon, The (2004)" +1292,3.1666666666666665,3,"Love Song for Bobby Long, A (2004)" +1293,3.75,4,"Merchant of Venice, The (2004)" +1294,1.5,2,Fat Albert (2004) +1295,3.0,1,"Keys to the House, The (Chiavi di casa, Le) (2004)" +1296,2.5,8,White Noise (2005) +1297,3.1666666666666665,3,"Upside of Anger, The (2005)" +1298,3.5,2,Stander (2003) +1299,3.0,2,Imaginary Heroes (2004) +1300,2.5,1,Ruby & Quentin (Tais-toi!) (2003) +1301,3.0,1,"Life and Death of Peter Sellers, The (2004)" +1302,3.8333333333333335,3,Appleseed (Appurushîdo) (2004) +1303,2.05,10,Elektra (2005) +1304,2.0,1,Racing Stripes (2005) +1305,3.0714285714285716,7,Coach Carter (2005) +1306,4.7,5,Memories of Murder (Salinui chueok) (2003) +1307,3.875,20,"Downfall (Untergang, Der) (2004)" +1308,3.1875,8,Assault on Precinct 13 (2005) +1309,0.5,1,Are We There Yet? (2005) +1310,0.5,1,Alone in the Dark (2005) +1311,2.3333333333333335,6,Hide and Seek (2005) +1312,2.0,2,Boogeyman (2005) +1313,3.1363636363636362,11,"Wedding Date, The (2005)" +1314,4.5,1,Rory O'Shea Was Here (Inside I'm Dancing) (2004) +1315,4.25,2,Nobody Knows (Dare mo shiranai) (2004) +1316,2.25,2,Employee of the Month (2004) +1317,4.0,1,Purple Butterfly (Zi hudie) (2003) +1318,4.075,40,Howl's Moving Castle (Hauru no ugoku shiro) (2004) +1319,3.0,2,Steamboy (Suchîmubôi) (2004) +1320,3.1666666666666665,45,Hitch (2005) +1321,0.5,1,Uncle Nino (2003) +1322,3.25,2,Bride & Prejudice (2004) +1323,3.4714285714285715,35,Constantine (2005) +1324,1.0,1,Son of the Mask (2005) +1325,3.1666666666666665,3,Because of Winn-Dixie (2005) +1326,4.0,1,Turtles Can Fly (Lakposhtha hâm parvaz mikonand) (2004) +1327,3.25,4,Night Watch (Nochnoy dozor) (2004) +1328,3.0,1,Man of the House (2005) +1329,3.5789473684210527,19,Kung Fu Hustle (Gong fu) (2004) +1330,4.0,1,Zelary (2003) +1331,3.5,1,Control (Kontroll) (2003) +1332,2.6666666666666665,3,Tyler Perry's Diary of a Mad Black Woman (2005) +1333,2.1666666666666665,6,Cursed (2005) +1334,2.7142857142857144,7,"Pacifier, The (2005)" +1335,2.4375,8,Be Cool (2005) +1336,3.6666666666666665,3,Gunner Palace (2004) +1337,3.05,10,Hostage (2005) +1338,3.026315789473684,19,Robots (2005) +1339,2.6666666666666665,3,Cube Zero (2004) +1340,3.5,1,Stealing Rembrandt (Rembrandt) (2003) +1341,3.375,4,Ice Princess (2005) +1342,3.1,5,Melinda and Melinda (2004) +1343,3.5,1,Milk and Honey (2003) +1344,2.25,8,Miss Congeniality 2: Armed and Fabulous (2005) +1345,2.3333333333333335,3,Guess Who (2005) +1346,2.8333333333333335,3,D.E.B.S. (2004) +1347,4.0,1,"League of Ordinary Gentlemen, A (2004)" +1348,3.5,1,Incident at Loch Ness (2004) +1349,2.5,1,800 Bullets (800 Balas) (2002) +1350,4.75,2,"Wild Parrots of Telegraph Hill, The (2003)" +1351,3.125,4,"Ballad of Jack and Rose, The (2005)" +1352,3.857142857142857,84,Sin City (2005) +1353,2.0,1,Beauty Shop (2005) +1354,3.0714285714285716,7,Sahara (2005) +1355,2.9375,8,Fever Pitch (2005) +1356,3.5,1,Eros (2004) +1357,3.5,1,Not on the Lips (Pas sur la bouche) (2003) +1358,2.5,1,Tanguy (2001) +1359,1.0,1,National Lampoon's Lady Killers (National Lampoon's Gold Diggers) (2003) +1360,3.0,1,Ringu 0: Bâsudei (2000) +1361,4.25,2,Brothers (Brødre) (2004) +1362,3.0,1,Carrie (2002) +1363,3.4204545454545454,44,"Hitchhiker's Guide to the Galaxy, The (2005)" +1364,2.25,6,"Amityville Horror, The (2005)" +1365,4.0,2,Mutant Aliens (2001) +1366,3.5,1,Before the Fall (NaPolA - Elite für den Führer) (2004) +1367,1.5,1,State Property 2 (2005) +1368,5.0,1,Palindromes (2004) +1369,3.0,6,"Lot Like Love, A (2005)" +1370,2.5,1,King's Ransom (2005) +1371,4.0,6,Enron: The Smartest Guys in the Room (2005) +1372,2.0,5,xXx: State of the Union (2005) +1373,3.5,17,Kingdom of Heaven (2005) +1374,1.8333333333333333,6,House of Wax (2005) +1375,3.89,50,Crash (2004) +1376,2.0,2,Mysterious Skin (2004) +1377,4.5,1,"Common Thread, A (a.k.a. Sequins) (Brodeuses) (2004)" +1378,3.6666666666666665,3,Dear Wendy (2005) +1379,3.5,1,Los Angeles Plays Itself (2003) +1380,3.4444444444444446,9,Unleashed (Danny the Dog) (2005) +1381,3.4294871794871793,78,Star Wars: Episode III - Revenge of the Sith (2005) +1382,2.642857142857143,7,Kicking & Screaming (2005) +1383,2.5,4,Monster-in-Law (2005) +1384,3.0,1,"Snow Walker, The (2003)" +1385,3.375,40,Madagascar (2005) +1386,3.3333333333333335,3,Mad Hot Ballroom (2005) +1387,2.5,1,Dominion: Prequel to the Exorcist (2005) +1388,3.111111111111111,9,"Longest Yard, The (2005)" +1389,5.0,1,Saving Face (2004) +1390,4.088235294117647,17,Cinderella Man (2005) +1391,3.0714285714285716,7,"Sisterhood of the Traveling Pants, The (2005)" +1392,3.0,7,Lords of Dogtown (2005) +1393,3.5,3,Rock School (2005) +1394,3.2796610169491527,59,Mr. & Mrs. Smith (2005) +1395,1.75,2,"Adventures of Sharkboy and Lavagirl 3-D, The (2005)" +1396,3.75,2,High Tension (Haute tension) (Switchblade Romance) (2003) +1397,3.1666666666666665,3,It's All Gone Pete Tong (2004) +1398,3.8620689655172415,116,Batman Begins (2005) +1399,3.0,1,Godzilla: Final Wars (Gojira: Fainaru uôzu) (2004) +1400,3.0,2,"Perfect Man, The (2005)" +1401,2.5,1,Saint Ralph (2004) +1402,3.1666666666666665,3,Herbie: Fully Loaded (2005) +1403,3.125,4,Land of the Dead (2005) +1404,2.269230769230769,13,Bewitched (2005) +1405,3.5,1,Rize (2005) +1406,3.75,4,Me and You and Everyone We Know (2005) +1407,4.5,1,"Perfect Crime, The (Crimen Ferpecto) (Ferpect Crime) (2004)" +1408,3.3333333333333335,3,3 Extremes (Three... Extremes) (Saam gaang yi) (2004) +1409,4.0,5,"Edukators, The (Die Fetten Jahre sind vorbei) (2004)" +1410,3.15,50,War of the Worlds (2005) +1411,3.5555555555555554,18,"March of the Penguins (Marche de l'empereur, La) (2005)" +1412,3.0,1,Rebound (2005) +1413,2.0,3,Dark Water (2005) +1414,3.0,2,"Beat That My Heart Skipped, The (battre mon coeur s'est arrêté, De) (2005)" +1415,2.763888888888889,36,Fantastic Four (2005) +1416,3.8333333333333335,3,Murderball (2005) +1417,3.5086206896551726,58,Wedding Crashers (2005) +1418,3.5,1,Happy Endings (2005) +1419,3.75,4,Hustle & Flow (2005) +1420,5.0,1,"Calcium Kid, The (2004)" +1421,3.3548387096774195,31,"Island, The (2005)" +1422,3.0,3,Bad News Bears (2005) +1423,3.7142857142857144,7,"Devil's Rejects, The (2005)" +1424,3.0,2,Last Days (2005) +1425,3.0,1,November (2004) +1426,2.9166666666666665,6,Sky High (2005) +1427,2.5,5,Stealth (2005) +1428,2.0,4,Must Love Dogs (2005) +1429,3.35,10,"Aristocrats, The (2005)" +1430,3.0,1,"Order, The (2001)" +1431,3.94,50,Serenity (2005) +1432,3.4761904761904763,21,Broken Flowers (2005) +1433,2.0833333333333335,12,"Dukes of Hazzard, The (2005)" +1434,2.0,3,The Chumscrubber (2005) +1435,3.6666666666666665,3,Junebug (2005) +1436,2.8333333333333335,3,Deuce Bigalow: European Gigolo (2005) +1437,3.0,4,"Skeleton Key, The (2005)" +1438,3.5714285714285716,7,Four Brothers (2005) +1439,3.1666666666666665,3,The Great Raid (2005) +1440,3.0,1,Pretty Persuasion (2005) +1441,3.7,5,Grizzly Man (2005) +1442,2.5,1,Pusher II: With Blood on My Hands (2004) +1443,3.5,1,Duma (2005) +1444,3.5472972972972974,74,"40-Year-Old Virgin, The (2005)" +1445,3.388888888888889,9,Red Eye (2005) +1446,2.5,2,Hidden (a.k.a. Cache) (Caché) (2005) +1447,1.5,1,Valiant (2005) +1448,2.625,20,"Brothers Grimm, The (2005)" +1449,4.0,1,"Baxter, The (2005)" +1450,2.6666666666666665,3,"Cave, The (2005)" +1451,3.4615384615384617,13,"Constant Gardener, The (2005)" +1452,3.21875,16,Transporter 2 (2005) +1453,2.6875,8,Just Like Heaven (2005) +1454,3.5714285714285716,7,Proof (2005) +1455,3.7142857142857144,35,Lord of War (2005) +1456,2.25,2,Cry_Wolf (a.k.a. Cry Wolf) (2005) +1457,3.7777777777777777,9,Everything Is Illuminated (2005) +1458,3.3333333333333335,3,Thumbsucker (2005) +1459,3.7083333333333335,12,Family Guy Presents Stewie Griffin: The Untold Story (2005) +1460,3.5,1,New Police Story (Xin jing cha gu shi) (2004) +1461,3.5,4,Why We Fight (2005) +1462,2.15,10,Doom (2005) +1463,2.875,8,Domino (2005) +1464,3.269230769230769,13,Waiting... (2005) +1465,2.761904761904762,21,Aeon Flux (2005) +1466,3.0,1,"Unfinished Life, An (2005)" +1467,2.5,1,"Man, The (2005)" +1468,3.5,1,Survive Style 5+ (2004) +1469,2.9166666666666665,6,"Exorcism of Emily Rose, The (2005)" +1470,2.8636363636363638,11,Flightplan (2005) +1471,3.534090909090909,44,Corpse Bride (2005) +1472,3.7222222222222223,9,Green Street Hooligans (a.k.a. Hooligans) (2005) +1473,3.5,27,"History of Violence, A (2005)" +1474,3.5,3,Oliver Twist (2005) +1475,3.3,5,"Greatest Game Ever Played, The (2005)" +1476,3.869565217391304,23,Capote (2005) +1477,3.55,10,Final Fantasy VII: Advent Children (2004) +1478,2.0,1,Roll Bounce (2005) +1479,2.5,2,Into the Blue (2005) +1480,3.611111111111111,9,MirrorMask (2005) +1481,3.6333333333333333,30,Wallace & Gromit in The Curse of the Were-Rabbit (2005) +1482,4.071428571428571,35,Kiss Kiss Bang Bang (2005) +1483,4.166666666666667,3,"Bittersweet Life, A (Dalkomhan insaeng) (2005)" +1484,4.0,1,Darwin's Nightmare (2004) +1485,2.5,1,Beowulf & Grendel (2005) +1486,4.625,4,No Direction Home: Bob Dylan (2005) +1487,4.25,2,Goal! The Dream Begins (Goal!) (2005) +1488,3.125,4,In Her Shoes (2005) +1489,3.4444444444444446,9,"Squid and the Whale, The (2005)" +1490,3.5,1,Two for the Money (2005) +1491,3.5588235294117645,34,Brokeback Mountain (2005) +1492,3.5,4,Elizabethtown (2005) +1493,3.5,4,North Country (2005) +1494,3.775,20,"Good Night, and Good Luck. (2005)" +1495,2.5,1,Dreamer: Inspired by a True Story (2005) +1496,3.5625,8,"Proposition, The (2005)" +1497,2.1,5,"Fog, The (2005)" +1498,3.0,7,Shopgirl (2005) +1499,3.0,4,Stay (2005) +1500,2.9,5,"Legend of Zorro, The (2005)" +1501,3.388888888888889,9,"Weather Man, The (2005)" +1502,2.8076923076923075,13,Saw II (2005) +1503,2.6,5,Prime (2005) +1504,4.5,1,Don't Move (Non ti muovere) (2004) +1505,2.142857142857143,7,American Pie Presents: Band Camp (American Pie 4: Band Camp) (2005) +1506,2.75,2,"Great Yokai War, The (Yôkai daisensô) (2005)" +1507,4.125,4,Manderlay (2005) +1508,3.357142857142857,7,Revolver (2005) +1509,3.676470588235294,17,Jarhead (2005) +1510,2.2857142857142856,7,Chicken Little (2005) +1511,4.75,2,Dead Man's Shoes (2004) +1512,3.7,5,Joyeux Noël (Merry Christmas) (2005) +1513,2.9166666666666665,6,Just Friends (2005) +1514,3.53125,16,Syriana (2005) +1515,2.0,1,One-Way Ticket to Mombasa (Menolippu Mombasaan) (2002) +1516,3.25,4,Derailed (2005) +1517,3.0,1,Creep (2004) +1518,3.5555555555555554,27,Pride & Prejudice (2005) +1519,3.25,4,Wolf Creek (2005) +1520,3.5454545454545454,11,"Descent, The (2005)" +1521,3.816901408450704,71,Harry Potter and the Goblet of Fire (2005) +1522,3.7564102564102564,39,Walk the Line (2005) +1523,3.15,10,Rent (2005) +1524,3.375,4,Zathura (2005) +1525,3.5,2,C.R.A.Z.Y. (2005) +1526,2.0,2,Sarah Silverman: Jesus Is Magic (2005) +1527,3.875,4,Breakfast on Pluto (2005) +1528,3.6666666666666665,3,"Ice Harvest, The (2005)" +1529,3.3333333333333335,3,"Yours, Mine and Ours (2005)" +1530,3.5,1,"Libertine, The (2004)" +1531,3.4411764705882355,17,Match Point (2005) +1532,4.0,2,Paradise Now (2005) +1533,3.443548387096774,62,"Chronicles of Narnia: The Lion, the Witch and the Wardrobe, The (2005)" +1534,3.4,40,King Kong (2005) +1535,3.5833333333333335,12,Memoirs of a Geisha (2005) +1536,2.875,8,"Family Stone, The (2005)" +1537,2.75,2,Havoc (2005) +1538,3.2142857142857144,7,"Matador, The (2005)" +1539,4.5,1,Wal-Mart: The High Cost of Low Price (2005) +1540,2.5,1,Mozart and the Whale (2005) +1541,4.0,5,"Three Burials of Melquiades Estrada, The (2006)" +1542,3.8095238095238093,21,Munich (2005) +1543,3.388888888888889,9,"Producers, The (2005)" +1544,3.5,6,Transamerica (2005) +1545,2.8333333333333335,3,Rumor Has It... (2005) +1546,1.875,4,Cheaper by the Dozen 2 (2005) +1547,2.1818181818181817,11,Fun with Dick and Jane (2005) +1548,2.75,4,"Ringer, The (2005)" +1549,3.0,3,Casanova (2005) +1550,2.0,2,Mrs. Henderson Presents (2005) +1551,3.2,5,"New World, The (2005)" +1552,4.5,1,Voices of a Distant Star (Hoshi no koe) (2003) +1553,3.5,1,"Boys of Baraka, The (2005)" +1554,4.0,5,Lady Vengeance (Sympathy for Lady Vengeance) (Chinjeolhan geumjassi) (2005) +1555,2.5,1,Grand Theft Parsons (2003) +1556,3.8,5,District 13 (Banlieue 13) (2004) +1557,2.8636363636363638,11,Hostel (2005) +1558,2.5714285714285716,7,Grandma's Boy (2006) +1559,3.6,5,Tristan & Isolde (2006) +1560,3.75,2,Glory Road (2006) +1561,3.5,3,Last Holiday (2006) +1562,3.25,4,Hoodwinked! (2005) +1563,3.09375,16,Underworld: Evolution (2006) +1564,3.5,1,Looking for Comedy in the Muslim World (2005) +1565,3.5,1,Water (2005) +1566,4.625,4,Sophie Scholl: The Final Days (Sophie Scholl - Die letzten Tage) (2005) +1567,3.7857142857142856,7,"World's Fastest Indian, The (2005)" +1568,1.0,1,Bandidas (2006) +1569,3.3333333333333335,3,Tristram Shandy: A Cock and Bull Story (2005) +1570,3.5,1,Helter Skelter (2004) +1571,2.75,2,Annapolis (2006) +1572,2.0,1,Big Momma's House 2 (2006) +1573,3.1666666666666665,6,Nanny McPhee (2005) +1574,2.8333333333333335,9,Final Destination 3 (2006) +1575,4.5,1,Something New (2006) +1576,3.1666666666666665,3,Block Party (a.k.a. Dave Chappelle's Block Party) (2005) +1577,3.5,1,Imagine Me & You (2005) +1578,2.857142857142857,7,"Pink Panther, The (2006)" +1579,3.5,2,Curious George (2006) +1580,2.75,8,Firewall (2006) +1581,1.3,5,When a Stranger Calls (2006) +1582,3.0,1,London (2005) +1583,2.0,1,Freedomland (2006) +1584,4.0,1,Winter Passing (2005) +1585,3.0,4,Eight Below (2006) +1586,0.8333333333333334,3,Date Movie (2006) +1587,3.3,5,Running Scared (2006) +1588,1.9230769230769231,13,Ultraviolet (2006) +1589,3.0,1,Just My Luck (2006) +1590,1.75,2,Pulse (2006) +1591,3.0,9,16 Blocks (2006) +1592,3.25,6,Failure to Launch (2006) +1593,2.75,2,Ultimate Avengers (2006) +1594,3.217391304347826,23,Ice Age 2: The Meltdown (2006) +1595,4.0,1,Ask the Dust (2006) +1596,3.885,100,V for Vendetta (2006) +1597,3.75,6,She's the Man (2006) +1598,4.027777777777778,36,Thank You for Smoking (2006) +1599,3.0,1,Find Me Guilty (2006) +1600,3.8,40,Inside Man (2006) +1601,3.25,4,Tsotsi (2005) +1602,2.0,2,Aquamarine (2006) +1603,3.5,1,Lights in the Dusk (Laitakaupungin valot) (2006) +1604,3.0833333333333335,6,"Hills Have Eyes, The (2006)" +1605,1.25,2,"Shaggy Dog, The (2006)" +1606,3.5,1,Unknown White Male (2005) +1607,4.117647058823529,34,"Lives of Others, The (Das leben der Anderen) (2006)" +1608,3.75,2,Take the Lead (2006) +1609,4.5,1,"Devil and Daniel Johnston, The (2005)" +1610,3.8552631578947367,38,Lucky Number Slevin (2006) +1611,3.8125,8,Volver (2006) +1612,3.8,5,Akeelah and the Bee (2006) +1613,2.5,1,Brainstorm (2001) +1614,1.3333333333333333,3,Stay Alive (2006) +1615,2.1666666666666665,3,Basic Instinct 2 (2006) +1616,3.875,12,Brick (2005) +1617,3.0,1,"Dead Hate the Living!, The (2000)" +1618,4.5,1,Evil Aliens (2005) +1619,3.5,6,This Film Is Not Yet Rated (2006) +1620,3.0,5,Slither (2006) +1621,2.772727272727273,11,"Benchwarmers, The (2006)" +1622,2.5,1,Renaissance (2006) +1623,5.0,1,Go for Zucker! (Alles auf Zucker!) (2004) +1624,3.25,4,Friends with Money (2006) +1625,4.75,2,Reefer Madness: The Movie Musical (2005) +1626,4.25,2,Candy (2006) +1627,4.0,1,"Child, The (L'enfant) (2005)" +1628,5.0,1,9/11 (2002) +1629,2.25,12,Scary Movie 4 (2006) +1630,3.5,12,Hard Candy (2005) +1631,3.6666666666666665,6,"Prairie Home Companion, A (2006)" +1632,2.75,2,"Sentinel, The (2006)" +1633,2.5,1,"Wild, The (2006)" +1634,2.9583333333333335,12,Silent Hill (2006) +1635,3.0,1,American Dreamz (2006) +1636,3.75,4,Kinky Boots (2005) +1637,3.0,1,"Protector, The (a.k.a. Warrior King) (Tom yum goong) (2005)" +1638,3.5892857142857144,28,Mission: Impossible III (2006) +1639,2.0,3,RV (2006) +1640,4.142857142857143,7,United 93 (2006) +1641,2.5833333333333335,6,Stick It (2006) +1642,1.5,2,"American Haunting, An (2005)" +1643,3.25,2,Down in the Valley (2005) +1644,3.4285714285714284,14,Over the Hedge (2006) +1645,3.3333333333333335,6,Art School Confidential (2006) +1646,2.875,4,Poseidon (2006) +1647,3.122448979591837,49,"Da Vinci Code, The (2006)" +1648,3.355769230769231,52,X-Men: The Last Stand (2006) +1649,2.590909090909091,11,"Break-Up, The (2006)" +1650,5.0,1,Peaceful Warrior (2006) +1651,3.3780487804878048,41,Cars (2006) +1652,3.0,2,"Notorious Bettie Page, The (2005)" +1653,2.5,1,Game 6 (2005) +1654,2.0,1,On a Clear Day (2005) +1655,2.3,5,"Omen, The (2006)" +1656,2.4642857142857144,14,Nacho Libre (2006) +1657,3.1,10,"Lake House, The (2006)" +1658,2.9782608695652173,23,Click (2006) +1659,3.5588235294117645,34,"Devil Wears Prada, The (2006)" +1660,3.5069444444444446,72,Pirates of the Caribbean: Dead Man's Chest (2006) +1661,2.1666666666666665,6,"You, Me and Dupree (2006)" +1662,3.8846153846153846,13,Clerks II (2006) +1663,2.8333333333333335,9,Lady in the Water (2006) +1664,1.5833333333333333,6,My Super Ex-Girlfriend (2006) +1665,3.1923076923076925,13,Marie Antoinette (2006) +1666,3.7,5,Who Killed the Electric Car? (2006) +1667,3.576923076923077,13,"Inconvenient Truth, An (2006)" +1668,2.0,3,High School Musical (2006) +1669,4.5,1,I Am a Sex Addict (2005) +1670,2.0,1,Stoned (2005) +1671,3.125,8,Jet Li's Fearless (Huo Yuan Jia) (2006) +1672,2.090909090909091,11,"Fast and the Furious: Tokyo Drift, The (Fast and the Furious 3, The) (2006)" +1673,2.25,2,Garfield: A Tail of Two Kitties (2006) +1674,4.0,2,Metal: A Headbanger's Journey (2005) +1675,3.08,25,Superman Returns (2006) +1676,3.5,1,"Road to Guantanamo, The (2006)" +1677,3.5,3,Edmond (2005) +1678,2.5,1,"OH in Ohio, The (2006)" +1679,3.883116883116883,77,Little Miss Sunshine (2006) +1680,3.5,31,Babel (2006) +1681,2.5,1,Strangers with Candy (2005) +1682,4.0,3,Wordplay (2006) +1683,1.0,1,Little Man (2006) +1684,3.111111111111111,9,Monster House (2006) +1685,2.642857142857143,14,Snakes on a Plane (2006) +1686,3.375,4,Scoop (2006) +1687,3.25,28,Talladega Nights: The Ballad of Ricky Bobby (2006) +1688,3.239130434782609,23,Night at the Museum (2006) +1689,2.6666666666666665,6,World Trade Center (2006) +1690,3.6931818181818183,44,Stranger than Fiction (2006) +1691,2.75,10,Miami Vice (2006) +1692,3.7934782608695654,46,"Pursuit of Happyness, The (2006)" +1693,2.7222222222222223,9,John Tucker Must Die (2006) +1694,3.25,2,"Ant Bully, The (2006)" +1695,3.4285714285714284,14,Crank (2006) +1696,4.0,1,"Secret Life of Words, The (2005)" +1697,3.5,1,Chaos (2005) +1698,2.8333333333333335,3,"Night Listener, The (2006)" +1699,3.5,4,Step Up (2006) +1700,2.5,1,Zoom (2006) +1701,4.25,2,Mind Game (2004) +1702,4.0,5,Half Nelson (2006) +1703,3.5,1,"Moustache, La (2005)" +1704,3.0,3,Tideland (2005) +1705,4.166666666666667,3,Adam's Apples (Adams æbler) (2005) +1706,2.0,1,Material Girls (2006) +1707,2.9285714285714284,14,Accepted (2006) +1708,3.7790697674418605,43,"Illusionist, The (2006)" +1709,3.730769230769231,13,The Queen (2006) +1710,3.5,6,Beerfest (2006) +1711,3.0,7,Invincible (2006) +1712,4.0,1,Idlewild (2006) +1713,3.5,3,Angel-A (2005) +1714,2.5,1,"Puffy Chair, The (2006)" +1715,1.0,4,"Wicker Man, The (2006)" +1716,3.75,4,"Wind That Shakes the Barley, The (2006)" +1717,3.0,1,Severance (2006) +1718,3.2,5,Hollywoodland (2006) +1719,3.0,1,"Covenant, The (2006)" +1720,3.8333333333333335,3,"Last Kiss, The (2006)" +1721,2.5,1,SherryBaby (2006) +1722,3.7222222222222223,18,Idiocracy (2006) +1723,4.071428571428571,7,Jesus Camp (2006) +1724,4.0,1,"Bow, The (Hwal) (2005)" +1725,4.0,1,"Tiger and the Snow, The (La tigre e la neve) (2005)" +1726,3.5,14,"Fountain, The (2006)" +1727,3.8636363636363638,11,"Science of Sleep, The (La science des rêves) (2006)" +1728,3.25,6,"Black Dahlia, The (2006)" +1729,3.0,1,"Woods, The (2006)" +1730,3.0,2,Gridiron Gang (2006) +1731,3.5,1,Land of Plenty (Angst and Alienation in America) (2004) +1732,4.0,1,"Bridge, The (2006)" +1733,3.6052631578947367,19,Apocalypto (2006) +1734,2.0,1,Flyboys (2006) +1735,3.5714285714285716,7,Jackass Number Two (2006) +1736,3.0,3,All the King's Men (2006) +1737,4.0,2,Conversations with Other Women (2005) +1738,3.3461538461538463,65,Borat: Cultural Learnings of America for Make Benefit Glorious Nation of Kazakhstan (2006) +1739,3.814814814814815,81,"Pan's Labyrinth (Laberinto del fauno, El) (2006)" +1740,3.0,4,"Guardian, The (2006)" +1741,3.0,2,Open Season (2006) +1742,2.5,1,School for Scoundrels (2006) +1743,4.252336448598131,107,"Departed, The (2006)" +1744,2.5,2,"Texas Chainsaw Massacre: The Beginning, The (2006)" +1745,2.8333333333333335,6,Employee of the Month (2006) +1746,2.5,1,Running With Scissors (2006) +1747,2.5,3,Man of the Year (2006) +1748,2.75,2,"Marine, The (2006)" +1749,3.75,2,Infamous (2006) +1750,2.75,2,Feast (2005) +1751,3.875,4,Little Children (2006) +1752,4.5,2,Deliver Us from Evil (2006) +1753,3.975,20,"Last King of Scotland, The (2006)" +1754,4.0,1,"U.S. vs. John Lennon, The (2006)" +1755,4.0,3,Shortbus (2006) +1756,3.9453125,64,Children of Men (2006) +1757,4.0055555555555555,90,"Prestige, The (2006)" +1758,3.4545454545454546,11,Flags of Our Fathers (2006) +1759,4.5,1,13 Tzameti (2005) +1760,3.0454545454545454,11,Saw III (2006) +1761,3.25,2,Catch a Fire (2006) +1762,3.25,2,Death of a President (2006) +1763,3.5,7,Flushed Away (2006) +1764,3.25,8,Perfume: The Story of a Murderer (2006) +1765,2.75,2,"Santa Clause 3: The Escape Clause, The (2006)" +1766,3.6666666666666665,3,"Good Year, A (2006)" +1767,3.0,3,Shut Up & Sing (2006) +1768,2.375,4,For Your Consideration (2006) +1769,3.0,1,Fuck (2005) +1770,4.0,1,Shooting Dogs (a.k.a. Beyond the Gates) (2005) +1771,3.9444444444444446,81,Casino Royale (2006) +1772,2.6818181818181817,11,Happy Feet (2006) +1773,1.3333333333333333,3,Let's Go to Prison (2006) +1774,3.3,20,Déjà Vu (Deja Vu) (2006) +1775,3.8333333333333335,3,Bobby (2006) +1776,2.6666666666666665,3,10 Items or Less (2006) +1777,3.2083333333333335,24,"Holiday, The (2006)" +1778,3.0,1,Harsh Times (2006) +1779,3.8333333333333335,3,Fast Food Nation (2006) +1780,3.4444444444444446,9,Tenacious D in The Pick of Destiny (2006) +1781,3.5,2,"Nativity Story, The (2006)" +1782,3.7641509433962264,53,Blood Diamond (2006) +1783,3.3333333333333335,3,Charlotte's Web (2006) +1784,2.3333333333333335,12,Eragon (2006) +1785,3.3214285714285716,14,Rocky Balboa (2006) +1786,3.0,1,Fur: An Imaginary Portrait of Diane Arbus (2006) +1787,3.5,1,Another Gay Movie (2006) +1788,4.0,4,"Painted Veil, The (2006)" +1789,3.75,2,We Are Marshall (2006) +1790,3.142857142857143,7,"Good Shepherd, The (2006)" +1791,3.1,5,Dreamgirls (2006) +1792,3.75,4,Freedom Writers (2007) +1793,4.0,1,When the Levees Broke: A Requiem in Four Acts (2006) +1794,3.6666666666666665,6,Inland Empire (2006) +1795,3.0,2,"History Boys, The (2006)" +1796,3.75,4,Notes on a Scandal (2006) +1797,2.0,2,DOA: Dead or Alive (2006) +1798,2.5,3,Curse of the Golden Flower (Man cheng jin dai huang jin jia) (2006) +1799,3.0,1,"Good German, The (2006)" +1800,3.7058823529411766,17,Letters from Iwo Jima (2006) +1801,2.0,1,Black Christmas (2006) +1802,2.5,2,Stomp the Yard (2007) +1803,3.75,2,Miss Potter (2006) +1804,1.1666666666666667,3,American Pie Presents The Naked Mile (American Pie 5: The Naked Mile) (2006) +1805,3.5,2,Venus (2006) +1806,2.0,1,Primeval (2007) +1807,3.125,8,Alpha Dog (2007) +1808,2.5,2,"Hitcher, The (2007)" +1809,3.5,1,After the Wedding (Efter brylluppet) (2006) +1810,2.7777777777777777,9,Bridge to Terabithia (2007) +1811,4.0,1,Beer League (2006) +1812,4.5,1,Dead Meat (2004) +1813,4.0,2,49 Up (2005) +1814,3.8333333333333335,6,Waitress (2007) +1815,2.25,2,Catch and Release (2006) +1816,3.1,10,Smokin' Aces (2006) +1817,2.0,2,Blood and Chocolate (2007) +1818,1.125,4,Epic Movie (2007) +1819,2.5,2,"Messengers, The (2007)" +1820,3.0,3,Because I Said So (2007) +1821,3.5,3,Hannibal Rising (2007) +1822,1.0,1,Norbit (2007) +1823,4.5,2,"Boss of It All, The (Direktøren for det hele) (2006)" +1824,4.5,2,Cocaine Cowboys (2006) +1825,3.8680555555555554,72,Ratatouille (2007) +1826,2.9375,8,"Paris, I Love You (Paris, je t'aime) (2006)" +1827,2.25,2,"Astronaut Farmer, The (2007)" +1828,3.5,1,"Wake Up, Ron Burgundy (2004)" +1829,1.0,2,It's a Boy Girl Thing (2006) +1830,5.0,1,"Ugly Duckling and Me!, The (2006)" +1831,3.0,4,Unknown (2006) +1832,2.25,12,Ghost Rider (2007) +1833,3.5625,8,Breach (2007) +1834,2.0,1,Tyler Perry's Daddy's Little Girls (2007) +1835,3.1,10,Music and Lyrics (2007) +1836,2.9285714285714284,14,"Number 23, The (2007)" +1837,3.0,2,Reno 911!: Miami (2007) +1838,3.5,4,Black Snake Moan (2006) +1839,4.5,1,My Father and My Son (Babam ve oglum) (2005) +1840,3.5,1,Factory Girl (2006) +1841,4.0,61,Hot Fuzz (2007) +1842,4.5,1,Golden Door (Nuovomondo) (2006) +1843,2.8214285714285716,14,Next (2007) +1844,3.0,1,Amazing Grace (2006) +1845,3.25,2,2001 Maniacs (2005) +1846,3.710526315789474,38,Zodiac (2007) +1847,2.5,1,Pusher III: I'm the Angel of Death (2005) +1848,2.3,5,Wild Hogs (2007) +1849,3.68125,80,300 (2007) +1850,2.0,1,"Abandoned, The (2006)" +1851,3.5,1,Starter for 10 (2006) +1852,3.0,2,"Last Mimzy, The (2007)" +1853,4.25,2,Priceless (Hors de prix) (2006) +1854,4.0,7,"Host, The (Gwoemul) (2006)" +1855,2.9166666666666665,6,Becoming Jane (2007) +1856,3.6666666666666665,3,"Namesake, The (2006)" +1857,2.5,1,I Think I Love My Wife (2007) +1858,3.5,3,Premonition (2007) +1859,3.25,2,Dead Silence (2007) +1860,4.1,5,Reign Over Me (2007) +1861,2.5,1,Pride (2007) +1862,3.86,25,Shooter (2007) +1863,2.5,2,"Hills Have Eyes II, The (2007)" +1864,3.0833333333333335,6,TMNT (Teenage Mutant Ninja Turtles) (2007) +1865,2.5,1,Black Book (Zwartboek) (2006) +1866,3.25,6,"Lookout, The (2007)" +1867,3.088235294117647,17,Blades of Glory (2007) +1868,1.5,1,Are We Done Yet? 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(2002) +2449,4.0,1,My Sister's Keeper (2009) +2450,4.5,1,Hood of Horror (2006) +2451,3.5,1,Watchmen: Tales of the Black Freighter (2009) +2452,3.6666666666666665,42,(500) Days of Summer (2009) +2453,3.65,10,Brüno (Bruno) (2009) +2454,3.75,2,"Librarian, The: The Curse of the Judas Chalice (2008)" +2455,3.8879310344827585,58,Harry Potter and the Half-Blood Prince (2009) +2456,5.0,1,Eichmann (2007) +2457,2.5,1,Open Water 2: Adrift (2006) +2458,3.8125,16,"Imaginarium of Doctor Parnassus, The (2009)" +2459,3.5,1,9to5: Days in Porn (a.k.a. 9 to 5: Days in Porn) (2008) +2460,2.5,1,Humpday (2009) +2461,1.5,1,Polytechnique (2009) +2462,3.0,4,Orphan (2009) +2463,3.6666666666666665,12,"Ugly Truth, The (2009)" +2464,3.5,1,"Collector, The (2009)" +2465,4.0,1,"Perfect Getaway, A (2009)" +2466,3.776923076923077,65,District 9 (2009) +2467,2.9583333333333335,12,Julie & Julia (2009) +2468,3.5,1,Obsessed (2009) +2469,3.5,2,Race to Witch Mountain (2009) +2470,3.5,1,Hannah Montana: The Movie (2009) +2471,2.625,8,G.I. Joe: The Rise of Cobra (2009) +2472,3.0,3,12 Rounds (2009) +2473,5.0,1,Max Manus (2008) +2474,3.6666666666666665,3,Lost in Austen (2008) +2475,3.8333333333333335,3,Evangelion: 1.0 You Are (Not) Alone (Evangerion shin gekijôban: Jo) (2007) +2476,2.0,1,"Deal, The (2008)" +2477,3.625,4,"Goods: Live Hard, Sell Hard, The (2009)" +2478,3.9,5,"Time Traveler's Wife, The (2009)" +2479,4.0,1,I Can't Think Straight (2007) +2480,2.8333333333333335,3,Miss March (2009) +2481,2.5,1,"I Love You, Beth Cooper (2009)" +2482,2.75,2,Paper Heart (2009) +2483,3.5,1,G-Force (2009) +2484,4.5,1,Tetro (2009) +2485,3.8333333333333335,3,Bronson (2009) +2486,3.75,2,It Might Get Loud (2008) +2487,2.0,1,My Life in Ruins (2009) +2488,3.0,3,Taking Woodstock (2009) +2489,3.0,3,Halloween II (2009) +2490,3.857142857142857,7,"Secret in Their Eyes, The (El secreto de sus ojos) (2009)" +2491,3.642857142857143,14,9 (2009) +2492,3.6,5,Frequently Asked Questions About Time Travel (2009) +2493,4.5,1,"White Ribbon, The (Das weiße Band) (2009)" +2494,3.5,1,Green Lantern: First Flight (2009) +2495,4.5,2,"Most Hated Family in America, The (2007)" +2496,3.4,5,Pandorum (2009) +2497,3.388888888888889,9,"Men Who Stare at Goats, The (2009)" +2498,3.0,1,"Hunt For Gollum, The (2009)" +2499,1.0,2,Jennifer's Body (2009) +2500,3.6666666666666665,3,"Informant!, The (2009)" +2501,2.0,2,Extract (2009) +2502,2.6666666666666665,3,"Final Destination, The (Final Destination 4) (Final Destination in 3-D, The) (2009)" +2503,3.4285714285714284,7,Gamer (2009) +2504,3.5294117647058822,17,Cloudy with a Chance of Meatballs (2009) +2505,5.0,1,Tyler Perry's I Can Do Bad All by Myself (2009) +2506,3.6875,8,"Food, Inc. (2008)" +2507,4.0,3,Thirst (Bakjwi) (2009) +2508,3.5,1,Bright Star (2009) +2509,3.0,1,Blood Creek (a.k.a. Town Creek) (2009) +2510,3.4,5,Paranormal Activity (2009) +2511,3.0,2,World's Greatest Dad (2009) +2512,1.0,1,Still Walking (Aruitemo aruitemo) (2008) +2513,4.25,4,"Cove, The (2009)" +2514,3.227272727272727,11,"Serious Man, A (2009)" +2515,4.0,1,City Island (2009) +2516,3.5,1,Ink (2009) +2517,2.5,1,Metropia (2009) +2518,3.5,1,"Haunted World of El Superbeasto, The (2009)" +2519,2.5,1,Trick 'r Treat (2007) +2520,3.1,5,Whip It (2009) +2521,2.3333333333333335,6,"Invention of Lying, The (2009)" +2522,3.0357142857142856,14,Surrogates (2009) +2523,3.8773584905660377,53,Zombieland (2009) +2524,1.0,1,Assassination of a High School President (2008) +2525,3.8333333333333335,6,"Education, An (2009)" +2526,4.0,1,Coco Before Chanel (Coco avant Chanel) (2009) +2527,3.5,1,Burma VJ: Reporting from a Closed Country (Burma VJ: Reporter i et lukket land) (2008) +2528,3.3333333333333335,3,Couples Retreat (2009) +2529,2.5,2,I Sell the Dead (2008) +2530,2.6666666666666665,12,Where the Wild Things Are (2009) +2531,3.5,4,"New York, I Love You (2009)" +2532,3.0625,8,Law Abiding Citizen (2009) +2533,3.0,1,"Misfortunates, The (De helaasheid der dingen) (2009)" +2534,4.2,10,Mary and Max (2009) +2535,3.5,1,Spread (2009) +2536,2.5,1,"Tournament, The (2009)" +2537,3.71875,32,Up in the Air (2009) +2538,3.5,3,Saw VI (2009) +2539,5.0,1,Love Exposure (Ai No Mukidashi) (2008) +2540,2.0,1,Cirque du Freak: The Vampire's Assistant (2009) +2541,3.375,4,"Boondock Saints II: All Saints Day, The (2009)" +2542,3.5,2,Black Dynamite (2009) +2543,4.5,1,Welcome to Dongmakgol (2005) +2544,2.25,2,Gentlemen Broncos (2009) +2545,4.083333333333333,18,Fantastic Mr. Fox (2009) +2546,3.0,2,"Christmas Carol, A (2009)" +2547,4.25,2,Battlestar Galactica: The Plan (2009) +2548,3.5,1,"Private Lives of Pippa Lee, The (2009)" +2549,4.375,4,Partly Cloudy (2009) +2550,2.619047619047619,21,2012 (2009) +2551,3.5,3,Precious (2009) +2552,2.25,2,Bad Lieutenant: Port of Call New Orleans (2009) +2553,2.6666666666666665,9,"Twilight Saga: New Moon, The (2009)" +2554,0.5,1,Derailed (2002) +2555,3.5,1,"Messenger, The (2009)" +2556,4.166666666666667,3,Ninja Assassin (2009) +2557,4.5,1,Cell 211 (Celda 211) (2009) +2558,3.0,1,Teenage Mutant Ninja Turtles: Turtles Forever (2009) +2559,3.0,1,Merry Madagascar (2009) +2560,3.5,5,Brothers (2009) +2561,3.5,1,Garage (2007) +2562,3.6774193548387095,31,"Blind Side, The (2009)" +2563,2.0,1,Shrink (2009) +2564,0.5,1,Old Dogs (2009) +2565,3.0,3,Planet 51 (2009) +2566,4.0,1,Earthlings (2006) +2567,3.8333333333333335,3,"Single Man, A (2009)" +2568,3.1666666666666665,3,"Lovely Bones, The (2009)" +2569,3.857142857142857,7,Invictus (2009) +2570,3.75,12,"Princess and the Frog, The (2009)" +2571,3.0,1,Did You Hear About the Morgans? 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Hachi: A Dog's Tale) (2009) +2583,2.4,5,Leap Year (2010) +2584,3.28125,16,"Book of Eli, The (2010)" +2585,4.2,5,"Girl Who Kicked the Hornet's Nest, The (Luftslottet som sprängdes) (2009)" +2586,4.25,4,"Prophet, A (Un Prophète) (2009)" +2587,1.5,1,Staten Island (2009) +2588,2.5,1,"Maiden Heist, The (2009)" +2589,2.0,1,Blood: The Last Vampire (2009) +2590,3.5,1,Bart Got a Room (2008) +2591,3.0,1,Robin-B-Hood (Bo bui gai wak) (2006) +2592,3.0,1,"Concert, Le (2009)" +2593,3.0,1,Ninja (2009) +2594,4.0,1,Asterix at the Olympic Games (Astérix aux jeux olympiques) (2008) +2595,3.0,1,"Chaser, The (Chugyeogja) (2008)" +2596,1.5,1,"Dennis the Menace Christmas, A (2007)" +2597,4.0,1,Undisputed II: Last Man Standing (2006) +2598,4.75,2,3 Idiots (2009) +2599,2.8333333333333335,3,Legion (2010) +2600,1.0,2,Stan Helsing (2009) +2601,2.5,4,When in Rome (2010) +2602,4.0,2,Triangle (2009) +2603,1.5,2,I Love You Phillip Morris (2009) +2604,4.375,4,Temple Grandin (2010) +2605,4.0,1,"House of the Devil, The (2009)" +2606,2.6666666666666665,3,Valentine's Day (2010) +2607,2.25,2,"Wolfman, The (2010)" +2608,4.022388059701493,67,Shutter Island (2010) +2609,3.3333333333333335,3,Persuasion (2007) +2610,4.25,4,"Girl Who Played with Fire, The (Flickan som lekte med elden) (2009)" +2611,2.357142857142857,7,Percy Jackson & the Olympians: The Lightning Thief (2010) +2612,2.5,5,Cop Out (2010) +2613,3.7857142857142856,7,"Ghost Writer, The (2010)" +2614,4.0,2,"Secret of Kells, The (2009)" +2615,2.0,1,"Spy Next Door, The (2010)" +2616,4.0,1,Agora (2009) +2617,1.0,1,Motherhood (2009) +2618,3.75,2,District 13: Ultimatum (Banlieue 13 - 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Shrek: The Final Chapter) (2010) +2705,3.75,2,TiMER (2009) +2706,4.0,1,Best Worst Movie (2009) +2707,2.45,10,"Twilight Saga: Eclipse, The (2010)" +2708,5.0,2,Enter the Void (2009) +2709,1.9166666666666667,6,"Last Airbender, The (2010)" +2710,3.0,1,Endgame (2009) +2711,4.5,1,Empire of Dreams: The Story of the 'Star Wars' Trilogy (2004) +2712,4.0,8,South Park: Imaginationland (2008) +2713,3.3,10,Predators (2010) +2714,4.0,1,When You're Strange (2009) +2715,3.6842105263157894,38,Despicable Me (2010) +2716,4.066433566433567,143,Inception (2010) +2717,3.45,10,Grown Ups (2010) +2718,3.45,10,"Sorcerer's Apprentice, The (2010)" +2719,3.4166666666666665,6,Knight and Day (2010) +2720,2.5,8,"Karate Kid, The (2010)" +2721,2.9166666666666665,6,"Kids Are All Right, The (2010)" +2722,2.25,2,"Serbian Film, A (Srpski film) (2010)" +2723,2.5,1,Cherrybomb (2009) +2724,3.6666666666666665,3,Batman: Under the Red Hood (2010) +2725,2.966666666666667,15,Salt (2010) +2726,3.0,1,"No. 1 Ladies' Detective Agency, The (2008)" +2727,4.142857142857143,7,Mr. Nobody (2009) +2728,3.2857142857142856,7,Dinner for Schmucks (2010) +2729,4.0,1,Deadly Outlaw: Rekka (a.k.a. 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The Final Chapter (2010) +2793,3.1,10,Due Date (2010) +2794,3.8333333333333335,18,127 Hours (2010) +2795,3.6,20,Megamind (2010) +2796,3.630952380952381,42,Black Swan (2010) +2797,3.357142857142857,7,Unstoppable (2010) +2798,2.6666666666666665,6,Morning Glory (2010) +2799,4.0,1,Certified Copy (Copie conforme) (2010) +2800,3.857142857142857,7,"Next Three Days, The (2010)" +2801,3.0,1,Somewhere (2010) +2802,4.25,2,Biutiful (2010) +2803,4.0,1,"First Beautiful Thing, The (La prima cosa bella) (2010)" +2804,3.9893617021276597,47,Harry Potter and the Deathly Hallows: Part 1 (2010) +2805,4.043103448275862,58,"King's Speech, The (2010)" +2806,3.9166666666666665,24,Tangled (2010) +2807,4.0,2,"Art of the Steal, The (2009)" +2808,3.760869565217391,23,"Fighter, The (2010)" +2809,3.0,1,"Romantics, The (2010)" +2810,4.25,2,"Loved Ones, The (2009)" +2811,4.0,2,Casino Jack (2010) +2812,3.0,1,Vincent Wants to Sea (Vincent will meer) (2010) +2813,1.5,1,London Boulevard (2010) +2814,0.5,1,Skyline (2010) +2815,3.5,1,Beastly (2011) +2816,2.7857142857142856,7,Love and Other Drugs (2010) +2817,3.857142857142857,7,"Chronicles of Narnia: The Voyage of the Dawn Treader, The (2010)" +2818,2.7142857142857144,7,"Tourist, The (2010)" +2819,4.25,2,Rare Exports: A Christmas Tale (Rare Exports) (2010) +2820,4.5,1,Hatchet II (2010) +2821,3.75,2,All Good Things (2010) +2822,3.75,28,True Grit (2010) +2823,3.236842105263158,19,Tron: Legacy (2010) +2824,2.6666666666666665,3,How Do You Know (2010) +2825,4.0,1,Barney's Version (2010) +2826,2.5,1,"Company Men, The (2010)" +2827,3.875,4,I Saw the Devil (Akmareul boatda) (2010) +2828,0.5,1,Trash Humpers (2009) +2829,5.0,1,Faster (2010) +2830,3.5,1,Little Big Soldier (Da bing xiao jiang) (2010) +2831,3.5,1,Rabbit Hole (2010) +2832,1.8333333333333333,6,Little Fockers (2010) +2833,1.8333333333333333,6,Gulliver's Travels (2010) +2834,4.5,1,Sweetgrass (2009) +2835,2.5,1,Burlesque (2010) +2836,3.5,4,"Secret World of Arrietty, The (Kari-gurashi no Arietti) (2010)" +2837,3.9166666666666665,18,Tucker & Dale vs Evil (2010) +2838,1.1666666666666667,3,Yogi Bear (2010) +2839,2.0,1,Made in Dagenham (2010) +2840,3.0,11,"Green Hornet, The (2011)" +2841,4.0,1,"Way Back, The (2010)" +2842,3.5,1,"Warrior's Way, The (2010)" +2843,3.5,1,Season of the Witch (2011) +2844,0.5,1,Amer (2009) +2845,3.0,9,Cowboys & Aliens (2011) +2846,2.5,1,Anything for Her (Pour elle) (2008) +2847,4.1,5,Day & Night (2010) +2848,4.25,2,Marwencol (2010) +2849,2.5,2,"Dilemma, The (2011)" +2850,4.0,1,"Trip, The (2010)" +2851,3.95,30,Limitless (2011) +2852,4.5,1,Happy People: A Year in the Taiga (2010) +2853,3.5,3,Evangelion: 2.0 You Can (Not) Advance (Evangerion shin gekijôban: Ha) (2009) +2854,3.75,2,I Spit on Your Grave (2010) +2855,4.0,1,Even the Rain (También la lluvia) (2010) +2856,5.0,1,Zeitgeist: Moving Forward (2011) +2857,2.9285714285714284,14,No Strings Attached (2011) +2858,3.6,10,"Lincoln Lawyer, The (2011)" +2859,4.25,2,All-Star Superman (2011) +2860,3.3333333333333335,6,Unknown (2011) +2861,2.25,2,Cedar Rapids (2011) +2862,3.0,1,Gnomeo & Juliet (2011) +2863,4.0,1,Burke and Hare (2010) +2864,4.0,1,Castaway on the Moon (Kimssi pyoryugi) (2009) +2865,3.2,15,Paul (2011) +2866,4.5,1,Brother 2 (Brat 2) (2000) +2867,2.5,2,Emma (2009) +2868,3.75,2,Drive Angry (2011) +2869,3.6176470588235294,17,Rango (2011) +2870,2.5,1,Take Me Home Tonight (2011) +2871,4.0,1,Confessions (Kokuhaku) (2010) +2872,3.1666666666666665,21,"Adjustment Bureau, The (2011)" +2873,3.375,4,"Mechanic, The (2011)" +2874,3.125,4,Hall Pass (2011) +2875,2.0,1,"Eagle, The (2011)" +2876,3.1,5,I Am Number Four (2011) +2877,2.375,4,Battle: Los Angeles (2011) +2878,3.8333333333333335,3,Summer Wars (Samâ wôzu) (2009) +2879,4.0,1,"Sunset Limited, The (2011)" +2880,3.5,1,Mars Needs Moms (2011) +2881,5.0,1,Scooby-Doo! Curse of the Lake Monster (2010) +2882,4.357142857142857,7,Elite Squad: The Enemy Within (Tropa de Elite 2 - O Inimigo Agora É Outro) (2010) +2883,3.5,3,Mesrine: Public Enemy #1 (L'ennemi public n°1) (2008) +2884,3.2142857142857144,7,Just Go with It (2011) +2885,3.0,1,Cave of Forgotten Dreams (2010) +2886,3.5,2,Red Riding Hood (2011) +2887,1.0,1,"Big Mommas: Like Father, Like Son (2011)" +2888,2.9,5,Super (2010) +2889,3.75,2,"Troll Hunter, The (Trolljegeren) (2010)" +2890,3.6029411764705883,34,Source Code (2011) +2891,3.0,1,Jane Eyre (2011) +2892,3.125,12,Sucker Punch (2011) +2893,4.5,3,BURN-E (2008) +2894,3.75,6,Senna (2010) +2895,3.5833333333333335,6,Family Guy Presents: It's a Trap (2010) +2896,2.6666666666666665,3,Insidious (2010) +2897,2.375,4,Hobo with a Shotgun (2011) +2898,3.75,6,Win Win (2011) +2899,2.75,2,Room in Rome (Habitación en Roma) (2010) +2900,4.0,1,Boy (2010) +2901,3.5,1,Diary of a Wimpy Kid: Rodrick Rules (2011) +2902,2.0,1,Henry's Crime (2010) +2903,2.75,2,Hop (2011) +2904,4.0,1,Playing the Victim (Izobrazhaya zhertvu) (2006) +2905,0.5,1,Films to Keep You Awake: The Christmas Tale (Películas para no dormir: Cuento de navidad) (2005) +2906,4.0,4,13 Assassins (Jûsan-nin no shikaku) (2010) +2907,3.3181818181818183,11,Hanna (2011) +2908,3.5,1,Into Eternity (2010) +2909,4.0,3,American: The Bill Hicks Story (2009) +2910,3.0,6,Arthur (2011) +2911,2.6666666666666665,3,Scream 4 (2011) +2912,3.125,8,Rio (2011) +2913,3.0714285714285716,7,Melancholia (2011) +2914,3.514705882352941,34,Thor (2011) +2915,4.055555555555555,9,Louis C.K.: Hilarious (2010) +2916,3.2857142857142856,7,Louis C.K.: Chewed Up (2008) +2917,2.0,1,Atlas Shrugged: Part 1 (2011) +2918,4.0,7,Louis C.K.: Shameless (2007) +2919,4.5,1,Mildred Pierce (2011) +2920,4.3,5,Voices from the List (2004) +2921,3.375,4,Water for Elephants (2011) +2922,2.5,1,African Cats (2011) +2923,4.0,2,Kill the Irishman (2011) +2924,3.05,10,"Fast Five (Fast and the Furious 5, The) (2011)" +2925,5.0,1,Louis Theroux: Law & Disorder (2008) +2926,5.0,1,Idiots and Angels (2008) +2927,4.4,5,Incendies (2010) +2928,3.5,1,Soul Surfer (2011) +2929,3.25,2,Something Borrowed (2011) +2930,3.761904761904762,21,Bridesmaids (2011) +2931,3.0,3,Priest (2011) +2932,3.2291666666666665,24,Pirates of the Caribbean: On Stranger Tides (2011) +2933,3.56,25,Midnight in Paris (2011) +2934,4.5,3,The Man from Nowhere (2010) +2935,4.0,2,"Tree of Life, The (2011)" +2936,3.2666666666666666,15,"Hangover Part II, The (2011)" +2937,4.0,1,Nothing to Declare (Rien à déclarer) (2010) +2938,2.0,1,Across the Hall (2009) +2939,2.0,1,"Roommate, The (2011)" +2940,3.5,6,Attack the Block (2011) +2941,3.5,2,The Way (2010) +2942,3.0,1,Let the Bullets Fly (2010) +2943,3.323529411764706,17,Kung Fu Panda 2 (2011) +2944,3.7906976744186047,43,X-Men: First Class (2011) +2945,4.166666666666667,3,Submarine (2010) +2946,4.0,1,American Grindhouse (2010) +2947,2.6666666666666665,3,Everything Must Go (2010) +2948,3.625,4,Beginners (2010) +2949,3.5952380952380953,21,Super 8 (2011) +2950,2.35,10,Green Lantern (2011) +2951,2.5,2,Elektra Luxx (2010) +2952,2.7,5,Mr. Popper's Penguins (2011) +2953,2.888888888888889,9,Bad Teacher (2011) +2954,2.3636363636363638,11,Transformers: Dark of the Moon (2011) +2955,2.75,2,Larry Crowne (2011) +2956,3.111111111111111,9,Your Highness (2011) +2957,4.5,1,Too Big to Fail (2011) +2958,2.75,2,Takers (2010) +2959,5.0,1,My Life as McDull (Mak dau goo si) (2001) +2960,2.75,4,Zookeeper (2011) +2961,3.4375,24,Horrible Bosses (2011) +2962,3.1666666666666665,3,Cars 2 (2011) +2963,3.0,1,Between the Folds (2008) +2964,4.0,1,Delhi Belly (2011) +2965,3.0,1,Upside Down: The Creation Records Story (2010) +2966,3.0,1,Monte Carlo (2011) +2967,3.91,50,Harry Potter and the Deathly Hallows: Part 2 (2011) +2968,3.765625,32,Drive (2011) +2969,3.546875,32,Captain America: The First Avenger (2011) +2970,3.9838709677419355,31,"Crazy, Stupid, Love. (2011)" +2971,3.5,1,One Day (2011) +2972,3.625,4,"Guard, The (2011)" +2973,3.0,1,Winnie the Pooh (2011) +2974,3.5,2,"Woman, The (2011)" +2975,4.0,1,Mike's New Car (2002) +2976,1.875,4,"Smurfs, The (2011)" +2977,3.05,20,Friends with Benefits (2011) +2978,5.0,1,Paper Birds (Pájaros de papel) (2010) +2979,3.0,1,Blitz (2011) +2980,2.5,1,"Yellow Sea, The (a.k.a. The Murderer) (Hwanghae) (2010)" +2981,3.25,2,Our Idiot Brother (2011) +2982,3.5,2,Death Race 2 (2010) +2983,3.462962962962963,27,Rise of the Planet of the Apes (2011) +2984,3.5,2,Terri (2011) +2985,2.85,10,"Change-Up, The (2011)" +2986,3.8333333333333335,21,"Help, The (2011)" +2987,2.6666666666666665,6,30 Minutes or Less (2011) +2988,3.0,1,My Afternoons with Margueritte (La tête en friche) (2010) +2989,4.5,1,Final Destination 5 (2011) +2990,3.25,2,"Very Harold & Kumar 3D Christmas, A (2011)" +2991,3.0,2,Don't Be Afraid of the Dark (2010) +2992,3.25,2,Fright Night (2011) +2993,4.5,1,Another Earth (2011) +2994,4.0,1,Hesher (2010) +2995,4.0,1,Stake Land (2010) +2996,2.5,1,"Debt, The (2011)" +2997,2.5,6,Colombiana (2011) +2998,4.5,1,Bill Cunningham New York (2011) +2999,3.0,4,"Skin I Live In, The (La piel que habito) (2011)" +3000,1.75,2,Conan the Barbarian (2011) +3001,3.5,1,Walled In (2009) +3002,3.5,1,Birdemic: Shock and Terror (2010) +3003,3.6666666666666665,3,"Inbetweeners Movie, The (2011)" +3004,3.0,1,Red State (2011) +3005,0.5,1,Pearl Jam Twenty (2011) +3006,1.5,1,I Don't Know How She Does It (2011) +3007,1.5,1,Shark Night 3D (2011) +3008,3.7083333333333335,12,Contagion (2011) +3009,3.8846153846153846,26,Moneyball (2011) +3010,4.0,1,Neds (2010) +3011,4.5,2,Cold Fish (Tsumetai nettaigyo) (2010) +3012,3.5,1,Phineas and Ferb the Movie: Across the 2nd Dimension (2011) +3013,4.0,1,Northanger Abbey (2007) +3014,3.869565217391304,69,"Avengers, The (2012)" +3015,3.8125,8,Tinker Tailor Soldier Spy (2011) +3016,4.333333333333333,3,"Separation, A (Jodaeiye Nader az Simin) (2011)" +3017,3.0,2,"Dangerous Method, A (2011)" +3018,3.727272727272727,11,Warrior (2011) +3019,3.2222222222222223,9,"Ides of March, The (2011)" +3020,4.5,1,Kill List (2011) +3021,2.8333333333333335,3,Killer Elite (2011) +3022,3.5,1,Bellflower (2011) +3023,3.6315789473684212,19,50/50 (2011) +3024,3.5,1,Generation P (2011) +3025,4.045454545454546,11,The Artist (2011) +3026,3.0,1,BlinkyTM (2011) +3027,3.75,2,Take Shelter (2011) +3028,3.0,6,Real Steel (2011) +3029,2.75,2,Footloose (2011) +3030,3.5,4,"Thing, The (2011)" +3031,4.0,1,Beautiful Boy (2010) +3032,3.5,1,Tyrannosaur (2011) +3033,3.75,2,Martha Marcy May Marlene (2011) +3034,3.5,4,We Need to Talk About Kevin (2011) +3035,2.0,1,Behind Enemy Lines II: Axis of Evil (2006) +3036,3.1666666666666665,3,"Three Musketeers, The (2011)" +3037,3.392857142857143,14,In Time (2011) +3038,3.0,1,Margaret (2011) +3039,4.0,4,Carnage (2011) +3040,3.7857142857142856,7,Margin Call (2011) +3041,3.5,2,Paranormal Activity 3 (2011) +3042,3.0,2,Puncture (2011) +3043,3.0,6,Johnny English Reborn (2011) +3044,3.5,1,Abduction (2011) +3045,3.5,1,This Must Be the Place (2011) +3046,3.75,4,Shame (2011) +3047,4.5,1,What's Your Number? (2011) +3048,3.6,5,Headhunters (Hodejegerne) (2011) +3049,3.6666666666666665,3,Batman: Year One (2011) +3050,3.5,1,Miss Representation (2011) +3051,3.5625,8,Puss in Boots (2011) +3052,3.5,4,Tower Heist (2011) +3053,2.5,1,J. Edgar (2011) +3054,1.5,1,"Double, The (2011)" +3055,3.5,12,"Adventures of Tintin, The (2011)" +3056,4.0,1,Starsuckers (2009) +3057,3.0,1,Tomboy (2011) +3058,4.0,1,George Harrison: Living in the Material World (2011) +3059,3.75,18,Hugo (2011) +3060,2.6666666666666665,3,Immortals (2011) +3061,2.3,5,Jack and Jill (2011) +3062,5.0,1,Into the Abyss (2011) +3063,4.0,11,"Descendants, The (2011)" +3064,2.5,2,Like Crazy (2011) +3065,2.6,10,"Muppets, The (2011)" +3066,2.2142857142857144,7,"Twilight Saga: Breaking Dawn - Part 1, The (2011)" +3067,3.0,3,War Horse (2011) +3068,3.5833333333333335,6,"Rum Diary, The (2011)" +3069,4.0,1,Lifted (2006) +3070,3.5,1,Hipsters (Stilyagi) (2008) +3071,2.5,1,Another Cinderella Story (2008) +3072,1.5,1,Bunraku (2010) +3073,2.9,5,"Sitter, The (2011)" +3074,2.75,2,Extremely Loud and Incredibly Close (2011) +3075,4.0,1,Play the Game (2009) +3076,5.0,1,Asterix and the Vikings (Astérix et les Vikings) (2006) +3077,5.0,1,Happy Feet Two (2011) +3078,0.5,1,Arthur Christmas (2011) +3079,3.0,2,Violet & Daisy (2011) +3080,1.5,1,Bullet to the Head (2012) +3081,3.5,10,"Expendables 2, The (2012)" +3082,3.435185185185185,54,The Hunger Games (2012) +3083,3.9934210526315788,76,"Dark Knight Rises, The (2012)" +3084,3.138888888888889,18,"Bourne Legacy, The (2012)" +3085,3.763157894736842,38,Sherlock Holmes: A Game of Shadows (2011) +3086,2.5,1,Coriolanus (2011) +3087,2.25,2,Young Adult (2011) +3088,2.0,3,New Year's Eve (2011) +3089,3.659090909090909,22,Mission: Impossible - Ghost Protocol (2011) +3090,3.3,5,We Bought a Zoo (2011) +3091,3.488095238095238,42,"Girl with the Dragon Tattoo, The (2011)" +3092,2.5,2,"Darkest Hour, The (2011)" +3093,1.0,2,Alvin and the Chipmunks: Chipwrecked (2011) +3094,1.5,1,Salvation Boulevard (2011) +3095,3.6666666666666665,3,Friends with Kids (2011) +3096,4.5,1,Girl Walks Into a Bar (2011) +3097,3.0,2,Contraband (2012) +3098,3.5,1,Being Elmo: A Puppeteer's Journey (2011) +3099,1.5,1,Joyful Noise (2012) +3100,3.0,1,"Iron Lady, The (2011)" +3101,2.5,1,Albatross (2011) +3102,4.5,1,"Revenant, The (2009)" +3103,3.8,5,Underworld: Awakening (2012) +3104,2.9166666666666665,6,"Grey, The (2012)" +3105,3.1666666666666665,3,Man on a Ledge (2012) +3106,2.5,1,Sacrifice (Zhao shi gu er) (2010) +3107,2.8333333333333335,3,Haywire (2011) +3108,3.0,1,Contact High (2009) +3109,3.25,2,"Whistleblower, The (2010)" +3110,3.5,1,Einstein and Eddington (2008) +3111,2.0,1,Apollo 18 (2011) +3112,2.0,1,Seeking Justice (2011) +3113,1.5,1,Red Tails (2012) +3114,4.5,1,"Flowers of War, The (Jin líng shí san chai) (2011)" +3115,4.108108108108108,37,Intouchables (2011) +3116,2.0,2,One for the Money (2012) +3117,2.75,2,"Innkeepers, The (2011)" +3118,3.1666666666666665,3,Grave Encounters (2011) +3119,3.375,8,Chronicle (2012) +3120,3.75,2,"Woman in Black, The (2012)" +3121,1.0,1,Woman in Love (Rubbeldiekatz) (2011) +3122,3.0,2,"Art of Getting By, The (2011)" +3123,4.25,2,All Watched Over by Machines of Loving Grace (2011) +3124,5.0,1,Dylan Moran: Monster (2004) +3125,3.375,4,Safe House (2012) +3126,2.6666666666666665,3,"Vow, The (2012)" +3127,4.3,10,Louis C.K.: Live at the Beacon Theater (2011) +3128,4.5,2,Monsieur Lazhar (2011) +3129,2.5,1,"For a Good Time, Call... (2012)" +3130,2.5,1,Janie Jones (2010) +3131,0.5,1,Journey 2: The Mysterious Island (2012) +3132,4.25,2,Perfect Sense (2011) +3133,2.0,1,Rollo and the Woods Sprite (Rölli ja metsänhenki) (2001) +3134,1.6,5,Ghost Rider: Spirit of Vengeance (2012) +3135,3.5,1,Prayers for Bobby (2009) +3136,4.25,2,"Very Potter Musical, A (2009)" +3137,5.0,1,"Very Potter Sequel, A (2010)" +3138,5.0,1,Miss Nobody (2010) +3139,3.5,1,"Women on the 6th Floor, The (Les Femmes du 6ème Étage) (2010)" +3140,1.5,1,Mega Shark vs. Crocosaurus (2010) +3141,3.0,1,Red Hill (2010) +3142,1.5,1,Gone (2012) +3143,2.0625,8,Project X (2012) +3144,3.1,5,Dr. Seuss' The Lorax (2012) +3145,3.75,4,"Big Year, The (2011)" +3146,3.75,2,Act of Valor (2012) +3147,2.6363636363636362,11,This Means War (2012) +3148,3.090909090909091,11,John Carter (2012) +3149,3.25,4,Goon (2011) +3150,2.5,1,"Ledge, The (2011)" +3151,3.8653846153846154,26,21 Jump Street (2012) +3152,4.0,1,"Jeff, Who Lives at Home (2012)" +3153,3.1666666666666665,3,Lockout (2012) +3154,2.5,1,"Snowtown (Snowtown Murders, The) (2011)" +3155,3.0,1,Space Battleship Yamato (2010) +3156,4.1,5,Jiro Dreams of Sushi (2011) +3157,2.25,2,Damsels in Distress (2011) +3158,1.5,1,Salmon Fishing in the Yemen (2011) +3159,2.625,4,Wrath of the Titans (2012) +3160,4.0,1,Detachment (2011) +3161,3.75,2,Iron Sky (2012) +3162,3.5,1,Absentia (2011) +3163,2.625,4,American Reunion (American Pie 4) (2012) +3164,4.333333333333333,9,The Raid: Redemption (2011) +3165,4.0227272727272725,22,"Cabin in the Woods, The (2012)" +3166,4.5,1,God Bless America (2011) +3167,1.25,2,"Three Stooges, The (2012)" +3168,2.0,1,"Raven, The (2012)" +3169,4.5,1,North & South (2004) +3170,1.5,1,"Big Bang, The (2011)" +3171,3.0,4,Mirror Mirror (2012) +3172,2.3333333333333335,3,Battleship (2012) +3173,3.875,4,"Best Exotic Marigold Hotel, The (2011)" +3174,2.0,1,Comic-Con Episode IV: A Fan's Hope (2011) +3175,3.5,2,Bully (2011) +3176,4.0,2,Hysteria (2011) +3177,3.0,1,Dante's Inferno: An Animated Epic (2010) +3178,2.5,4,"Five-Year Engagement, The (2012)" +3179,2.5,2,Think Like a Man (2012) +3180,4.0,1,"Lucky One, The (2012)" +3181,3.5,2,Safe (2012) +3182,2.5,6,Dark Shadows (2012) +3183,2.5,1,96 Minutes (2011) +3184,3.6666666666666665,3,"Decoy Bride, The (2011)" +3185,4.0,1,Rocket Singh: Salesman of the Year (2009) +3186,3.5588235294117645,17,"Dictator, The (2012)" +3187,4.0,1,Walking with Monsters (2005) +3188,3.2777777777777777,18,Men in Black III (M.III.B.) (M.I.B.³) (2012) +3189,2.8333333333333335,6,Snow White and the Huntsman (2012) +3190,3.5,1,Sound of My Voice (2011) +3191,5.0,1,Eva (2011) +3192,3.3333333333333335,3,"Pirates! Band of Misfits, The (2012)" +3193,3.2962962962962963,27,Prometheus (2012) +3194,3.5,1,"Pact, The (2012)" +3195,3.5,2,Bernie (2011) +3196,2.5,1,Inhale (2010) +3197,3.5,1,Take This Waltz (2011) +3198,3.0,5,Wanderlust (2012) +3199,3.7758620689655173,29,Moonrise Kingdom (2012) +3200,3.8333333333333335,3,Get the Gringo (2012) +3201,4.0,1,Superman/Doomsday (2007) +3202,2.8333333333333335,3,"Thousand Words, A (2012)" +3203,3.75,6,Safety Not Guaranteed (2012) +3204,3.388888888888889,9,Madagascar 3: Europe's Most Wanted (2012) +3205,4.0,1,Your Sister's Sister (2011) +3206,5.0,1,Superman/Batman: Public Enemies (2009) +3207,3.466666666666667,30,Brave (2012) +3208,5.0,1,Front of the Class (2008) +3209,3.0,1,What to Expect When You're Expecting (2012) +3210,3.0,1,To Rome with Love (2012) +3211,2.75,6,Abraham Lincoln: Vampire Hunter (2012) +3212,3.5,1,First Position (2011) +3213,2.75,4,Rock of Ages (2012) +3214,4.166666666666667,3,Seeking a Friend for the End of the World (2012) +3215,5.0,1,Presto (2008) +3216,4.5,1,Jack-Jack Attack (2005) +3217,3.8333333333333335,3,One Man Band (2005) +3218,3.2857142857142856,21,Ted (2012) +3219,2.7,5,Magic Mike (2012) +3220,3.5,1,Cleanskin (2012) +3221,3.25,30,"Amazing Spider-Man, The (2012)" +3222,2.9166666666666665,6,Ice Age 4: Continental Drift (2012) +3223,3.357142857142857,7,Beasts of the Southern Wild (2012) +3224,2.75,2,Savages (2012) +3225,3.0,1,"Firm, The (2009)" +3226,4.5,1,Spirit Camp (2009) +3227,4.5,1,Some Guy Who Kills People (2011) +3228,2.0,1,Treasure Island (2012) +3229,3.0,9,"Watch, The (2012)" +3230,2.5,1,2 Days in New York (2012) +3231,4.5,1,Killer Joe (2011) +3232,0.5,1,Anaconda: The Offspring (2008) +3233,3.9,5,For the Birds (2000) +3234,3.5,4,Ruby Sparks (2012) +3235,3.1875,8,Total Recall (2012) +3236,4.0,1,"Angels' Share, The (2012)" +3237,3.5,1,"Immature, The (Immaturi) (2011)" +3238,3.6666666666666665,3,Sidewalls (Medianeras) (2011) +3239,3.81,50,Skyfall (2012) +3240,3.4444444444444446,9,"Campaign, The (2012)" +3241,2.0,1,Brake (2012) +3242,1.0,1,Hope Springs (2012) +3243,2.0,1,"Queen of Versailles, The (2012)" +3244,3.5,2,ParaNorman (2012) +3245,3.0,1,Diary of a Wimpy Kid: Dog Days (2012) +3246,4.5,1,Broken (2012) +3247,4.0,1,6 Days to Air: The Making of South Park (2011) +3248,3.2,5,Premium Rush (2012) +3249,5.0,1,"Odd Life of Timothy Green, The (2012)" +3250,3.625,4,Lawless (2012) +3251,2.1666666666666665,3,Piranha 3DD (a.k.a. Piranha DD) (2012) +3252,4.25,4,Searching for Sugar Man (2012) +3253,3.0,1,Prime Suspect 6: The Last Witness (2003) +3254,2.5,1,Conception (2011) +3255,4.0,1,Paradise Lost 3: Purgatory (2011) +3256,4.0,1,"Words, The (2012)" +3257,3.625,20,Pitch Perfect (2012) +3258,3.8333333333333335,3,Samsara (2011) +3259,3.640625,32,Looper (2012) +3260,2.5,3,That's My Boy (2012) +3261,3.0,3,Robot & Frank (2012) +3262,3.75,2,Resident Evil: Retribution (2012) +3263,1.5,1,Lola Versus (2012) +3264,3.9166666666666665,6,"Master, The (2012)" +3265,3.7142857142857144,14,Dredd (2012) +3266,3.9166666666666665,6,End of Watch (2012) +3267,2.75,2,V/H/S (2012) +3268,3.825,20,"Perks of Being a Wallflower, The (2012)" +3269,4.3,10,"Hunt, The (Jagten) (2012)" +3270,5.0,1,Holy Motors (2012) +3271,3.2857142857142856,7,Taken 2 (2012) +3272,4.0,1,House at the End of the Street (2012) +3273,5.0,1,My Left Eye Sees Ghosts (Ngo joh aan gin diy gwai) (2002) +3274,3.5,1,Love Lasts Three Years (L'amour dure trois ans) (2011) +3275,1.5,1,"Tall Man, The (2012)" +3276,3.5,1,LOL (2012) +3277,0.5,1,Rust and Bone (De rouille et d'os) (2012) +3278,3.5,2,Marley (2012) +3279,2.5,2,Frankenweenie (2012) +3280,4.0,1,Sinister (2012) +3281,3.409090909090909,11,Hotel Transylvania (2012) +3282,3.0,1,Side by Side (2012) +3283,3.982142857142857,28,Argo (2012) +3284,3.466666666666667,15,Seven Psychopaths (2012) +3285,3.0,1,Liberal Arts (2012) +3286,1.25,2,Catch .44 (2011) +3287,3.0,2,[REC]³ 3 Génesis (2012) +3288,2.5,1,Asterix & Obelix: God Save Britannia (Astérix et Obélix: Au service de Sa Majesté) (2012) +3289,3.0,1,Paranormal Activity 4 (2012) +3290,1.5,1,Alex Cross (2012) +3291,3.736842105263158,19,Cloud Atlas (2012) +3292,4.0,1,'Hellboy': The Seeds of Creation (2004) +3293,3.0,1,Silent Hill: Revelation 3D (2012) +3294,2.5714285714285716,7,Here Comes the Boom (2012) +3295,2.0,1,Mental (2012) +3296,3.125,4,Killing Them Softly (2012) +3297,4.75,2,"Imposter, The (2012)" +3298,4.0,1,"Sessions, The (Surrogate, The) (2012)" +3299,4.0,1,Smashed (2012) +3300,3.75,22,Wreck-It Ralph (2012) +3301,3.716666666666667,30,Silver Linings Playbook (2012) +3302,3.5,9,Flight (2012) +3303,3.0,1,Anna Karenina (2012) +3304,3.629032258064516,31,Life of Pi (2012) +3305,2.5,1,"Man with the Iron Fists, The (2012)" +3306,2.5,1,"Bay, The (2012)" +3307,3.5,1,Himizu (2011) +3308,4.0,2,Jackass 3.5 (2011) +3309,3.0,2,Indie Game: The Movie (2012) +3310,3.9285714285714284,7,"Batman: The Dark Knight Returns, Part 1 (2012)" +3311,4.5,4,Lincoln (2012) +3312,1.5,1,Nature Calls (2012) +3313,1.5,1,Vamps (2012) +3314,1.875,4,"Twilight Saga: Breaking Dawn - Part 2, The (2012)" +3315,3.5,1,10 Years (2011) +3316,2.25,2,Red Dawn (2012) +3317,3.7916666666666665,12,Rise of the Guardians (2012) +3318,4.0,2,"Fantastic Fear of Everything, A (2012)" +3319,1.5,1,Deadfall (2012) +3320,4.0,1,Byzantium (2012) +3321,4.375,8,Paperman (2012) +3322,3.5,5,Hitchcock (2012) +3323,4.5,1,From Up on Poppy Hill (Kokuriko-zaka kara) (2011) +3324,4.5,1,Redline (2009) +3325,2.5,1,"Liar's Autobiography: The Untrue Story of Monty Python's Graham Chapman, A (2012)" +3326,3.8125,40,"Hobbit: An Unexpected Journey, The (2012)" +3327,2.0,1,Hyde Park on Hudson (2012) +3328,1.5,1,How to Make Love to a Woman (2010) +3329,4.107142857142857,14,Zero Dark Thirty (2012) +3330,1.5,1,Fire with Fire (2012) +3331,3.5,14,Warm Bodies (2013) +3332,4.0,1,Wrong (2012) +3333,3.0,1,Playing for Keeps (2012) +3334,2.5,3,"Guilt Trip, The (2012)" +3335,3.4166666666666665,12,Jack Reacher (2012) +3336,3.943661971830986,71,Django Unchained (2012) +3337,3.125,8,This Is 40 (2012) +3338,3.6666666666666665,3,"Impossible, The (Imposible, Lo) (2012)" +3339,3.5,7,"Misérables, Les (2012)" +3340,2.0,1,Parental Guidance (2012) +3341,4.0,2,John Dies at the End (2012) +3342,2.25,2,"Misérables, Les (2000)" +3343,3.0,1,Promised Land (2012) +3344,5.0,1,English Vinglish (2012) +3345,3.0,1,Fish Story (Fisshu sutôrî) (2009) +3346,1.5,1,Texas Chainsaw 3D (2013) +3347,3.3333333333333335,3,Gangster Squad (2013) +3348,3.5,1,"Iceman, The (2012)" +3349,4.25,2,It's Such a Beautiful Day (2012) +3350,3.875,8,"Batman: The Dark Knight Returns, Part 2 (2013)" +3351,3.0,1,Everything or Nothing: The Untold Story of 007 (2012) +3352,4.0,1,Codependent Lesbian Space Alien Seeks Same (2011) +3353,3.25,2,"Last Stand, The (2013)" +3354,3.1666666666666665,3,Upstream Color (2013) +3355,3.0,1,Shadow Dancer (2012) +3356,4.0,1,Human Planet (2011) +3357,3.5,1,Comme un chef (2012) +3358,3.5,3,Movie 43 (2013) +3359,3.5,1,"Pervert's Guide to Ideology, The (2012)" +3360,4.5,1,Sightseers (2012) +3361,2.9,5,Hansel & Gretel: Witch Hunters (2013) +3362,4.5,1,Jim Jefferies: Fully Functional (EPIX) (2012) +3363,1.5,1,Why Stop Now (2012) +3364,4.0,1,Tabu (2012) +3365,3.0,1,Upside Down (2012) +3366,3.0,1,"Liability, The (2012)" +3367,2.5,1,Stand Up Guys (2012) +3368,3.9166666666666665,6,Side Effects (2013) +3369,2.875,4,Identity Thief (2013) +3370,3.5,1,"ABCs of Death, The (2012)" +3371,2.0,1,Beautiful Creatures (2013) +3372,2.0833333333333335,6,"Good Day to Die Hard, A (2013)" +3373,3.625,4,21 and Over (2013) +3374,4.0,1,Safe Haven (2013) +3375,4.5,2,Frozen Planet (2011) +3376,5.0,1,"Act of Killing, The (2012)" +3377,4.0,1,Universal Soldier: Day of Reckoning (2012) +3378,4.0,2,Escape from Planet Earth (2013) +3379,3.5,3,Before Midnight (2013) +3380,3.5,1,Snitch (2013) +3381,3.0,2,Dark Skies (2013) +3382,3.5,1,Oh Boy (A Coffee in Berlin) (2012) +3383,4.75,2,Journey to the West: Conquering the Demons (Daai wa sai you chi Chui mo chun kei) (2013) +3384,2.2,5,Jack the Giant Slayer (2013) +3385,2.5,1,Wadjda (2012) +3386,2.5,1,"Unintentional Kidnapping of Mrs. Elfriede Ott, The (Die Unabsichtliche Entführung der Frau Elfriede Ott) (2010)" +3387,3.0,3,G.I. Joe: Retaliation (2013) +3388,3.75,2,Stoker (2013) +3389,3.0833333333333335,6,Oz the Great and Powerful (2013) +3390,3.7777777777777777,9,"Croods, The (2013)" +3391,2.8333333333333335,3,"Incredible Burt Wonderstone, The (2013)" +3392,1.5,1,"Call, The (2013)" +3393,2.875,8,Olympus Has Fallen (2013) +3394,1.5,2,"First Time, The (2012)" +3395,3.5,5,"Place Beyond the Pines, The (2012)" +3396,2.0,1,"Brass Teapot, The (2012)" +3397,2.0,1,Phil Spector (2013) +3398,2.5,4,"Host, The (2013)" +3399,3.0,4,Admission (2013) +3400,3.25,2,Evil Dead (2013) +3401,3.75,2,Trance (2013) +3402,4.25,2,"Perfect Plan, A (Plan parfait, Un) (2012)" +3403,3.3,20,Oblivion (2013) +3404,1.5,1,Dark Tide (2012) +3405,4.0,1,42 (2013) +3406,4.0,4,Wolf Children (Okami kodomo no ame to yuki) (2012) +3407,1.5,1,Disconnect (2012) +3408,3.0,1,"Invincible Iron Man, The (2007)" +3409,3.2,5,Pain & Gain (2013) +3410,3.0,1,Hulk Vs. (2009) +3411,4.0,1,Resolution (2012) +3412,3.0,1,Grabbers (2012) +3413,5.0,1,Justice League: Doom (2012) +3414,2.0,1,"Grandmaster, The (Yi dai zong shi) (2013)" +3415,3.2142857142857144,14,This Is the End (2013) +3416,3.5625,32,Iron Man 3 (2013) +3417,2.0,1,"English Teacher, The (2013)" +3418,4.333333333333333,3,Mud (2012) +3419,2.0,1,Pawn (2013) +3420,2.75,2,Syrup (2013) +3421,3.375,20,"Great Gatsby, The (2013)" +3422,3.685185185185185,27,Star Trek Into Darkness (2013) +3423,3.55,10,"Internship, The (2013)" +3424,3.0,1,Darkon (2006) +3425,3.75,2,Only God Forgives (2013) +3426,3.375,8,"Hangover Part III, The (2013)" +3427,3.357142857142857,7,"Fast & Furious 6 (Fast and the Furious 6, The) (2013)" +3428,3.0,2,Epic (2013) +3429,4.0,1,Tie Xi Qu: West of the Tracks (Tiexi qu) (2003) +3430,4.0,1,Down Terrace (2009) +3431,2.5,1,Frances Ha (2012) +3432,2.5,1,"Lords of Salem, The (2012)" +3433,3.0,1,Behind the Candelabra (2013) +3434,3.5,1,As I Was Moving Ahead Occasionally I Saw Brief Glimpses of Beauty (2000) +3435,3.0,1,With Great Power: The Stan Lee Story (2012) +3436,1.0,2,After Earth (2013) +3437,3.409090909090909,22,Now You See Me (2013) +3438,4.5,1,Inhuman Resources (Redd Inc.) 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(2013) +3474,4.5,1,"Field in England, A (2013)" +3475,4.214285714285714,7,"Conjuring, The (2013)" +3476,2.5,1,Turbo (2013) +3477,3.375,16,"Wolverine, The (2013)" +3478,2.3333333333333335,3,Drinking Buddies (2013) +3479,3.2,5,Red 2 (2013) +3480,2.25,2,Coffee Town (2013) +3481,2.0,1,Revenge for Jolly! (2012) +3482,4.0,3,2 Guns (2013) +3483,3.375,4,Blue Jasmine (2013) +3484,3.5,3,"Great Beauty, The (Grande Bellezza, La) (2013)" +3485,4.125,4,Louis C.K.: Oh My God (2013) +3486,1.75,2,Percy Jackson: Sea of Monsters (2013) +3487,1.875,4,"Smurfs 2, The (2013)" +3488,4.0,1,Alan Partridge: Alpha Papa (2013) +3489,3.25,2,Man of Tai Chi (2013) +3490,3.0,1,Batman: Mystery of the Batwoman (2003) +3491,3.738095238095238,21,We're the Millers (2013) +3492,3.4,5,Grown Ups 2 (2013) +3493,3.4545454545454546,11,Kick-Ass 2 (2013) +3494,3.3,5,Riddick (2013) +3495,3.0,2,Planes (2013) +3496,3.0,2,Blackfish (2013) +3497,4.0,3,"Wind Rises, The (Kaze tachinu) (2013)" +3498,2.5,3,Jobs (2013) +3499,1.5,1,Lee Daniels' The Butler (2013) +3500,4.0,2,In a World... (2013) +3501,3.933333333333333,15,About Time (2013) +3502,3.25,2,Justice League: Crisis on Two Earths (2010) +3503,3.75,2,You're Next (2011) +3504,0.5,1,Maria Bamford: The Special Special Special! 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(Jigoku de naze warui) (2013) +3636,4.5,1,Ocho apellidos vascos (2014) +3637,3.7419354838709675,31,Captain America: The Winter Soldier (2014) +3638,2.8,5,Noah (2014) +3639,4.333333333333333,3,"Nut Job, The (2014)" +3640,3.5,1,13 Sins (2014) +3641,3.3333333333333335,3,Muppets Most Wanted (2014) +3642,4.5,1,Me and you (io e te) (2012) +3643,4.5,1,Free to Play (2014) +3644,4.0,1,"Unknown Known, The (2013)" +3645,3.9285714285714284,7,The Raid 2: Berandal (2014) +3646,2.6363636363636362,11,The Amazing Spider-Man 2 (2014) +3647,4.0,1,Calvary (2014) +3648,4.5,1,Oculus (2013) +3649,0.5,1,God's Not Dead (2014) +3650,2.5,1,Cold in July (2014) +3651,2.0,2,Rio 2 (2014) +3652,3.75,2,"Honest Liar, An (2014)" +3653,4.0,1,Fading Gigolo (2013) +3654,2.2857142857142856,7,Transcendence (2014) +3655,4.0,1,Hatchet III (2013) +3656,2.9166666666666665,6,"Other Woman, The (2014)" +3657,0.5,1,"Haunted House 2, A (2014)" +3658,2.5,1,Mulan II (2004) +3659,1.5,1,Brick Mansions (2014) +3660,3.8333333333333335,3,Locke (2013) +3661,3.2916666666666665,12,Neighbors (2014) +3662,4.5,1,Alpha and Omega 3: The Great Wolf Games (2014) +3663,1.5,1,Mom's Night Out (2014) +3664,2.8214285714285716,14,Lucy (2014) +3665,3.8333333333333335,30,X-Men: Days of Future Past (2014) +3666,2.6153846153846154,13,Godzilla (2014) +3667,2.5,1,Walk of Shame (2014) +3668,4.0,1,Blue Ruin (2013) +3669,2.75,2,Chef (2014) +3670,3.5,1,Afflicted (2013) +3671,3.8125,8,Blended (2014) +3672,4.0,3,Begin Again (2013) +3673,2.75,16,Maleficent (2014) +3674,2.5,2,Zombeavers (2014) +3675,1.5,1,At Middleton (2013) +3676,3.5,1,"Dance of Reality, The (Danza de la realidad, La) (2013)" +3677,2.9583333333333335,12,A Million Ways to Die in the West (2014) +3678,3.977272727272727,44,Edge of Tomorrow (2014) +3679,3.6470588235294117,17,Mission: Impossible - Rogue Nation (2015) +3680,0.5,1,Midnight Chronicles (2009) +3681,3.0,1,Million Dollar Arm (2014) +3682,3.0,1,G.B.F. (2013) +3683,3.5,1,Jimi: All Is by My Side (2013) +3684,3.5,1,Bad Asses (Bad Ass 2) (2014) +3685,4.5,1,Lilting (2014) +3686,3.388888888888889,9,The Fault in Our Stars (2014) +3687,3.8333333333333335,3,Tangled Ever After (2012) +3688,3.5,1,Maps to the Stars (2014) +3689,3.6842105263157894,19,22 Jump Street (2014) +3690,3.2777777777777777,9,"Equalizer, The (2014)" +3691,3.7666666666666666,15,How to Train Your Dragon 2 (2014) +3692,3.3461538461538463,26,Birdman: Or (The Unexpected Virtue of Ignorance) (2014) +3693,3.823529411764706,17,Boyhood (2014) +3694,1.5,1,Think Like a Man Too (2014) +3695,2.5,1,Jersey Boys (2014) +3696,4.5,1,"Internet's Own Boy: The Story of Aaron Swartz, The (2014)" +3697,1.875,4,Transformers: Age of Extinction (2014) +3698,4.333333333333333,3,Frank (2014) +3699,2.0,1,They Came Together (2014) +3700,3.5,1,Honey (Miele) (2013) +3701,2.25,2,Planes: Fire & Rescue (2014) +3702,2.0,2,Tammy (2014) +3703,5.0,1,Colourful (Karafuru) (2010) +3704,4.5,3,"Babadook, The (2014)" +3705,4.0394736842105265,38,Whiplash (2014) +3706,3.7162162162162162,37,Gone Girl (2014) +3707,4.0,1,"Angriest Man in Brooklyn, The (2014)" +3708,3.433333333333333,15,Dawn of the Planet of the Apes (2014) +3709,3.0,1,Deliver Us from Evil (2014) +3710,2.0,1,And So It Goes (2014) +3711,2.6,5,Sex Tape (2014) +3712,3.8333333333333335,3,I Origins (2014) +3713,3.3,5,"Purge: Anarchy, The (2014)" +3714,4.0508474576271185,59,Guardians of the Galaxy (2014) +3715,4.5,1,"Signal, The (2014)" +3716,3.5,3,The Expendables 3 (2014) +3717,2.0,4,Hercules (2014) +3718,4.5,1,A Most Wanted Man (2014) +3719,3.5,1,Life After Beth (2014) +3720,3.0,1,Felony (2013) +3721,3.5,1,Get on Up (2014) +3722,3.0,1,Magic in the Moonlight (2014) +3723,4.25,2,Housebound (2014) +3724,4.5,1,The Hundred-Foot Journey (2014) +3725,3.0,1,Batman: Assault on Arkham (2014) +3726,4.0,1,White Frog (2012) +3727,3.5,1,"Den, The (2013)" +3728,2.0,2,Jupiter Ascending (2015) +3729,2.75,4,Teenage Mutant Ninja Turtles (2014) +3730,1.5,1,I'll Follow You Down (2013) +3731,2.3,5,"Giver, The (2014)" +3732,4.0,1,"Pretty One, The (2013)" +3733,1.5,1,Revenge of the Green Dragons (2014) +3734,3.1,5,Let's Be Cops (2014) +3735,3.5,2,"Inbetweeners 2, The (2014)" +3736,4.0,1,"Sacrament, The (2013)" +3737,3.25,4,Sin City: A Dame to Kill For (2014) +3738,2.0,1,If I Stay (2014) +3739,3.5,1,"Two Days, One Night (Deux jours, une nuit) (2014)" +3740,4.0,2,Coherence (2013) +3741,3.0,1,"As Above, So Below (2014)" +3742,5.0,1,"One I Love, The (2014)" +3743,2.5,1,Headshot (2011) +3744,4.0,1,"Guest, The (2014)" +3745,4.0,2,Pride (2014) +3746,3.5,1,Honeymoon (2014) +3747,2.8333333333333335,3,The Drop (2014) +3748,4.0,1,"20,000 Days on Earth (2014)" +3749,3.3333333333333335,3,The Skeleton Twins (2014) +3750,2.5,1,Beautiful Losers (2008) +3751,2.9375,16,"Maze Runner, The (2014)" +3752,3.5,1,Camp X-Ray (2014) +3753,3.0,1,"Walk Among the Tombstones, A (2014)" +3754,5.0,1,Laggies (2014) +3755,1.5,1,Cesar Chavez (2014) +3756,1.5,1,Who Am I (Kein System Ist Sicher) (2014) +3757,1.0,1,"Tale of Princess Kaguya, The (Kaguyahime no monogatari) (2013)" +3758,2.75,2,This Is Where I Leave You (2014) +3759,3.8,15,American Sniper (2014) +3760,3.5,1,Tusk (2014) +3761,4.25,2,Hector and the Search for Happiness (2014) +3762,3.0,1,Horns (2014) +3763,3.0,2,Annabelle (2014) +3764,2.8333333333333335,3,Two Night Stand (2014) +3765,3.5,4,Dracula Untold (2014) +3766,2.1666666666666665,3,Stretch (2014) +3767,3.0,1,Autómata (Automata) (2014) +3768,2.0,1,"Captive, The (2014)" +3769,3.9,10,Predestination (2014) +3770,2.0,1,Justin and the Knights of Valour (2013) +3771,3.5,1,Ward 13 (2003) +3772,4.833333333333333,3,What We Do in the Shadows (2014) +3773,3.8448275862068964,29,John Wick (2014) +3774,2.0,1,Plastic (2014) +3775,2.5,1,"Judge, The (2014)" +3776,3.0,1,"Culture High, The (2014)" +3777,3.769230769230769,13,Fury (2014) +3778,3.0,1,"Salvation, The (2014)" +3779,3.25,2,St. Vincent (2014) +3780,3.5,1,"Rewrite, The (2014)" +3781,4.166666666666667,18,Nightcrawler (2014) +3782,3.8536585365853657,41,Big Hero 6 (2014) +3783,4.0,2,The Book of Life (2014) +3784,3.5,1,"Love, Rosie (2014)" +3785,3.8333333333333335,3,Time Lapse (2014) +3786,3.9107142857142856,28,Ex Machina (2015) +3787,3.0,1,Mr Hublot (2013) +3788,4.5,1,Copenhagen (2014) +3789,3.6666666666666665,3,"Simpsons: The Longest Daycare, The (2012)" +3790,4.0,1,Generation War (2013) +3791,2.0,1,Leviathan (2014) +3792,2.5,1,Reign of Assassins (2010) +3793,1.5,1,Zulu (2013) +3794,4.5,1,Tangerines (2013) +3795,3.0,1,Life Partners (2014) +3796,1.5,1,Drive Hard (2014) +3797,3.0,1,New Kids Nitro (2011) +3798,3.0,1,Stalingrad (2013) +3799,4.0,2,Dead Snow 2: Red vs. Dead (2014) +3800,4.0,1,You Are the Apple of My Eye (2011) +3801,3.5,1,DeadHeads (2011) +3802,4.02,50,The Imitation Game (2014) +3803,3.6666666666666665,6,Inherent Vice (2014) +3804,4.5,1,Rudderless (2014) +3805,3.869565217391304,23,The Hunger Games: Mockingjay - Part 1 (2014) +3806,2.5,1,Sex Ed (2014) +3807,2.75,2,Exodus: Gods and Kings (2014) +3808,4.25,10,Wild Tales (2014) +3809,2.3333333333333335,3,Dumb and Dumber To (2014) +3810,1.5,1,The Longest Week (2014) +3811,2.5,1,Miss Meadows (2014) +3812,3.0,2,Too Many Cooks (2014) +3813,3.5,1,Painted Skin (2008) +3814,3.7058823529411766,17,The Theory of Everything (2014) +3815,4.5,3,Doctor Who: The Time of the Doctor (2013) +3816,4.0,1,Virunga (2014) +3817,3.5,1,The Madagascar Penguins in a Christmas Caper (2005) +3818,3.0,1,Song of the Sea (2014) +3819,3.5,3,In the Heart of the Sea (2015) +3820,3.0,1,Hello Ladies: The Movie (2014) +3821,3.260869565217391,23,Jurassic World (2015) +3822,5.0,1,Watermark (2014) +3823,3.875,4,Citizenfour (2014) +3824,3.75,2,Asterix: The Land of the Gods (Astérix: Le domaine des dieux) (2014) +3825,2.5,1,Hit by Lightning (2014) +3826,3.6363636363636362,11,Horrible Bosses 2 (2014) +3827,4.5,1,Dragonheart 2: A New Beginning (2000) +3828,3.7,5,Penguins of Madagascar (2014) +3829,4.0,1,'71 (2014) +3830,3.0,1,The Rabbi's Cat (Le chat du rabbin) (2011) +3831,3.8,5,Still Alice (2014) +3832,4.0,4,Paddington (2014) +3833,1.8333333333333333,6,Maze Runner: Scorch Trials (2015) +3834,3.5,1,Ice Age: A Mammoth Christmas (2011) +3835,3.5,1,The Voices (2014) +3836,2.5,1,The Green Prince (2014) +3837,1.5,1,Dying of the Light (2014) +3838,5.0,1,Hellbenders (2012) +3839,2.0,1,By the Gun (2014) +3840,3.0,1,Kill the Messenger (2014) +3841,1.5,1,Bring It On: In It To Win It (2007) +3842,3.5,2,The Mule (2014) +3843,3.4166666666666665,18,The Hobbit: The Battle of the Five Armies (2014) +3844,2.5,2,Selma (2014) +3845,2.5,3,Unbroken (2014) +3846,4.0,1,Black Sea (2015) +3847,3.5,1,Good Copy Bad Copy (2007) +3848,2.0,1,Playing It Cool (2014) +3849,5.0,1,National Lampoon's Bag Boy (2007) +3850,4.0,1,Closer to the Moon (2013) +3851,3.5,2,"Girl Walks Home Alone at Night, A (2014)" +3852,4.25,2,Dave Chappelle: For What it's Worth (2004) +3853,5.0,1,Scooby-Doo! Abracadabra-Doo (2010) +3854,4.0,1,Mommy (2014) +3855,2.9,5,Wild (2014) +3856,3.0,2,Top Five (2014) +3857,4.5,1,Bill Burr: I'm Sorry You Feel That Way (2014) +3858,3.75,2,Big Eyes (2014) +3859,2.8,5,Into the Woods (2014) +3860,2.0,1,"Men, Women & Children (2014)" +3861,3.4583333333333335,12,The Interview (2014) +3862,3.986111111111111,36,Kingsman: The Secret Service (2015) +3863,4.5,1,Bill Burr: You People Are All the Same (2012) +3864,3.5,6,Night at the Museum: Secret of the Tomb (2014) +3865,3.5,1,Paradox (2010) +3866,2.5,1,The Punisher: Dirty Laundry (2012) +3867,2.25,2,Seventh Son (2014) +3868,3.5,1,Corner Gas: The Movie (2014) +3869,2.0,1,A Merry Friggin' Christmas (2014) +3870,5.0,1,Into the Forest of Fireflies' Light (2011) +3871,5.0,1,PK (2014) +3872,3.25,10,Chappie (2015) +3873,4.333333333333333,3,The Salt of the Earth (2014) +3874,4.5,1,The Fool (2014) +3875,2.7,5,Taken 3 (2015) +3876,2.5,2,Blackhat (2015) +3877,2.5,1,Son of a Gun (2014) +3878,2.5,9,Terminator Genisys (2015) +3879,4.0,1,John Mulaney: New In Town (2012) +3880,4.5,1,Patton Oswalt: My Weakness Is Strong (2009) +3881,3.0,1,Man on High Heels (2014) +3882,4.0,1,Space Buddies (2009) +3883,2.0,1,Houdini (2014) +3884,2.5,1,101 Dalmatians II: Patch's London Adventure (2003) +3885,2.5,1,The Hungover Games (2014) +3886,3.5,1,The Duke of Burgundy (2014) +3887,4.0,1,Red Army (2014) +3888,4.125,4,It Follows (2014) +3889,3.0,1,The Town that Dreaded Sundown (2014) +3890,4.5,1,Bill Burr: Let It Go (2010) +3891,4.5,1,Bill Burr: Why Do I Do This? (2008) +3892,4.5,1,Killer Movie (2008) +3893,3.5,1,Sebastian Maniscalco: What's Wrong with People? 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The Movie (2003) +3980,2.875,4,Hot Tub Time Machine 2 (2015) +3981,4.5,1,Johnny Express (2014) +3982,2.0,1,Northmen - A Viking Saga (2014) +3983,0.5,1,Superfast! (2015) +3984,4.0,1,Reality (2014) +3985,1.5,1,Julia (2014) +3986,3.25,10,Focus (2015) +3987,2.75,2,Marvel One-Shot: Item 47 (2012) +3988,3.0,2,The Second Best Exotic Marigold Hotel (2015) +3989,5.0,1,George Carlin: It's Bad for Ya! (2008) +3990,1.0,1,Tracers (2015) +3991,3.5,1,"McFarland, USA (2015)" +3992,2.25,2,Unfinished Business (2015) +3993,2.5,1,Ghost in the Shell Arise - Border 1: Ghost Pain (2013) +3994,2.1666666666666665,3,Run All Night (2015) +3995,3.0,1,Digging Up the Marrow (2014) +3996,3.5,1,Clown (2014) +3997,4.0,1,Cinderella (2015) +3998,2.0,1,Kidnapping Mr. Heineken (2015) +3999,3.75,2,The Cobbler (2015) +4000,4.0,1,Ruby Red (2013) +4001,2.5,3,Pan (2015) +4002,4.0,1,While We're Young (2014) +4003,2.8333333333333335,6,Insurgent (2015) +4004,4.0,3,The Amazing Screw-On Head (2006) +4005,3.25,8,Home (2015) +4006,2.375,4,Midnight Special (2015) +4007,4.0,1,"Gunman, The (2015)" +4008,2.75,6,Furious 7 (2015) +4009,3.0,1,The Final Girls (2015) +4010,4.5,1,Spring (2015) +4011,4.0,1,Power/Rangers (2015) +4012,5.0,1,George Carlin: Life Is Worth Losing (2005) +4013,4.5,1,Legend No. 17 (2013) +4014,3.125,8,Get Hard (2015) +4015,4.5,1,That Sugar Film (2014) +4016,5.0,1,Saving Santa (2013) +4017,5.0,1,Tom and Jerry: A Nutcracker Tale (2007) +4018,5.0,1,What Men Talk About (2010) +4019,3.0,1,Kill Me Three Times (2014) +4020,2.0,1,Poker Night (2014) +4021,3.0,2,Shaun the Sheep Movie (2015) +4022,3.0,3,Last Knights (2015) +4023,5.0,1,The Jinx: The Life and Deaths of Robert Durst (2015) +4024,3.0,1,Batman vs. Robin (2015) +4025,3.0,1,Libre et assoupi (2014) +4026,4.0,1,Woman in Gold (2015) +4027,3.75,2,Iliza Shlesinger: Freezing Hot (2015) +4028,4.5,1,The Road Within (2014) +4029,2.5,7,Tomorrowland (2015) +4030,5.0,1,Buzzard (2015) +4031,2.75,4,Paul Blart: Mall Cop 2 (2015) +4032,5.0,1,Seve (2014) +4033,1.0,1,Breathe (2014) +4034,3.25,2,Da Sweet Blood of Jesus (2014) +4035,1.75,2,The Longest Ride (2015) +4036,3.5,1,Girltrash: All Night Long (2014) +4037,1.0,1,Sword of Vengeance (2014) +4038,2.0,1,Lovesick (2014) +4039,4.5,1,Danny Collins (2015) +4040,3.5,1,The Even Stevens Movie (2003) +4041,1.5,1,Kite (2014) +4042,3.75,2,Man Up (2015) +4043,1.875,4,San Andreas (2015) +4044,3.0,2,Welcome to Me (2014) +4045,3.0,1,Comedy Central Roast of James Franco (2013) +4046,4.0,1,We Could Be King (2014) +4047,1.5,1,Hitman: Agent 47 (2015) +4048,3.5,1,B/W (2015) +4049,2.0,1,Ricki and the Flash (2015) +4050,2.0,1,Partisan (2015) +4051,1.5,1,Infini (2015) +4052,3.4444444444444446,9,Pitch Perfect 2 (2015) +4053,1.0,1,Just Before I Go (2014) +4054,4.0,1,Carol (2015) +4055,4.0,7,The Lobster (2015) +4056,3.0,1,Güeros (2014) +4057,2.0,1,Maggie (2015) +4058,1.5,1,Hot Pursuit (2015) +4059,4.25,2,Slow West (2015) +4060,3.5,1,The Green Inferno (2014) +4061,4.5,1,Barely Lethal (2015) +4062,5.0,1,What Love Is (2007) +4063,2.5,1,5 to 7 (2014) +4064,5.0,1,My Love (2006) +4065,5.0,1,Radio Day (2008) +4066,4.0,48,The Martian (2015) +4067,1.5,1,Return to Sender (2015) +4068,3.7142857142857144,7,Kung Fury (2015) +4069,4.5,1,Elections Day (2007) +4070,3.5,4,Youth (2015) +4071,0.5,1,Survivor (2015) +4072,3.0,1,Hot Girls Wanted (2015) +4073,4.5,1,Phir Hera Pheri (2006) +4074,3.6875,16,Spy (2015) +4075,3.2,10,Trainwreck (2015) +4076,3.0,1,Turtle Power: The Definitive History of the Teenage Mutant Ninja Turtles (2014) +4077,0.5,1,Aloha (2015) +4078,2.0,1,Dragon Blade (2015) +4079,2.0,1,Entourage (2015) +4080,5.0,1,Bitter Lake (2015) +4081,2.0,1,No Way Jose (2015) +4082,5.0,1,Ghost Graduation (2012) +4083,3.813953488372093,43,Inside Out (2015) +4084,3.5,1,The Wolfpack (2015) +4085,2.5,1,Trevor Noah: African American (2013) +4086,4.333333333333333,3,Love & Mercy (2014) +4087,3.7,10,The Hunger Games: Mockingjay - Part 2 (2015) +4088,2.5833333333333335,6,Pixels (2015) +4089,3.6785714285714284,14,Fantastic Beasts and Where to Find Them (2016) +4090,4.0,1,The Hairdresser (2010) +4091,3.0,1,Mr. Holmes (2015) +4092,3.0714285714285716,7,The Secret Life of Pets (2016) +4093,3.25,2,Ghost in the Shell: Stand Alone Complex - The Laughing Man (2005) +4094,2.75,2,Self/less (2015) +4095,3.3333333333333335,3,The Last Witch Hunter (2015) +4096,3.5,1,Krampus (2015) +4097,2.9166666666666665,12,Suicide Squad (2016) +4098,2.375,4,Independence Day: Resurgence (2016) +4099,3.466666666666667,15,Star Trek Beyond (2016) +4100,4.5,1,Golmaal (2006) +4101,3.5,1,Bruce Lee: A Warrior's Journey (2000) +4102,2.7777777777777777,9,Ted 2 (2015) +4103,3.0,1,Absolutely Anything (2015) +4104,3.1666666666666665,15,Minions (2015) +4105,3.0,4,The Good Dinosaur (2015) +4106,2.5,3,Black Mass (2015) +4107,3.0588235294117645,17,Spectre (2015) +4108,1.0,2,Sharknado 3: Oh Hell No! (2015) +4109,5.0,1,Scooby-Doo! and the Samurai Sword (2009) +4110,5.0,1,Scooby-Doo! and the Loch Ness Monster (2004) +4111,5.0,1,Big Top Scooby-Doo! (2012) +4112,1.5,1,Gabriel Iglesias: Hot and Fluffy (2007) +4113,3.75,2,Ghost in the Shell 2.0 (2008) +4114,3.5,1,Kevin Hart: Laugh at My Pain (2011) +4115,5.0,1,Tom and Jerry: Shiver Me Whiskers (2006) +4116,2.0,1,Jeff Dunham: All Over the Map (2014) +4117,4.5,1,The FP (2012) +4118,5.0,1,Kung Fu Panda: Secrets of the Masters (2011) +4119,3.75,6,Steve Jobs (2015) +4120,2.5,1,Macbeth (2015) +4121,3.4166666666666665,6,Vacation (2015) +4122,3.0,2,Creep (2014) +4123,1.5,1,The Face of an Angel (2015) +4124,1.0,1,Wild Horses (2015) +4125,2.25,2,Search Party (2014) +4126,2.0,1,The Squeeze (2015) +4127,1.5,1,Careful What You Wish For (2015) +4128,1.5,1,Robot Overlords (2014) +4129,2.5,1,Bad Asses on the Bayou (2015) +4130,5.0,1,The Eye: Infinity (2005) +4131,2.5,1,Kiss me Kismet (2006) +4132,3.5,1,Da geht noch was! (2013) +4133,1.5,1,The Lovers (2015) +4134,2.34375,16,Batman v Superman: Dawn of Justice (2016) +4135,2.0,1,God Loves Caviar (2012) +4136,3.5,4,Amy (2015) +4137,2.0,1,That Demon Within (2014) +4138,4.0,1,Magic Mike XXL (2015) +4139,3.6363636363636362,11,The Jungle Book (2016) +4140,4.0,1,Dragon Ball Z: Resurrection of F (2015) +4141,3.4722222222222223,18,The Man from U.N.C.L.E. (2015) +4142,0.5,1,Sorrow (2015) +4143,4.0,2,7 Days in Hell (2015) +4144,3.0,2,The Walk (2015) +4145,3.3,5,13 Hours (2016) +4146,3.5,1,There Will Come a Day (2013) +4147,1.5,1,The Opposite Sex (2014) +4148,1.5,1,The Gallows (2015) +4149,5.0,1,Tokyo Tribe (2014) +4150,3.0,1,Feast (2014) +4151,0.5,1,Joe Dirt 2: Beautiful Loser (2015) +4152,5.0,1,Nasu: Summer in Andalusia (2003) +4153,2.0,1,Dark Places (2015) +4154,2.5,1,Afro Samurai (2007) +4155,4.5,1,Massu Engira Maasilamani (2015) +4156,3.903225806451613,31,The Revenant (2015) +4157,3.0,1,Irrational Man (2015) +4158,3.0,1,Exte: Hair Extensions (2007) +4159,5.0,1,Ooops! Noah is Gone... (2015) +4160,2.8333333333333335,6,Southpaw (2015) +4161,3.5454545454545454,11,Sicario (2015) +4162,3.0,1,Goodnight Mommy (Ich seh ich seh) (2014) +4163,1.25,2,10 Cent Pistol (2015) +4164,3.5,1,Before We Go (2014) +4165,3.25,2,Anomalisa (2015) +4166,1.5,1,Colonia (2016) +4167,2.5,1,Ghost in the Shell Arise - Border 2: Ghost Whispers (2013) +4168,2.25,2,How to Make Love Like an Englishman (2014) +4169,2.0,1,Always Watching: A Marble Hornets Story (2015) +4170,3.6538461538461537,13,The Intern (2015) +4171,4.5,1,Love (2015) +4172,3.5625,8,Room (2015) +4173,2.0,1,The Runner (2015) +4174,3.25,4,The Gift (2015) +4175,4.0,1,The Witch (2015) +4176,4.0,1,Men & Chicken (2015) +4177,3.0,1,The Escort (2015) +4178,4.0,4,Doctor Who: The Waters of Mars (2009) +4179,4.5,1,"Family Guy Presents: Something, Something, Something, Dark Side (2009)" +4180,3.5,1,"Visit, The (2015)" +4181,1.5,1,Secret in Their Eyes (2015) +4182,4.0,1,Jeff Ross Roasts Criminals: Live at Brazos County Jail (2015) +4183,5.0,1,Battle For Sevastopol (2015) +4184,2.6666666666666665,6,American Ultra (2015) +4185,4.214285714285714,7,Straight Outta Compton (2015) +4186,3.5,2,Cop Car (2015) +4187,3.5,1,The Lost Room (2006) +4188,4.5,1,Tangerine (2015) +4189,2.0,1,Every Secret Thing (2014) +4190,3.2,5,Joy (2015) +4191,3.25,2,Victor Frankenstein (2015) +4192,2.5,1,Guardians (2016) +4193,3.75,2,Scouts Guide to the Zombie Apocalypse (2015) +4194,2.5,1,Suffragette (2015) +4195,4.0,1,Fort Tilden (2014) +4196,3.5,3,Turbo Kid (2015) +4197,3.5,1,The Unauthorized Saved by the Bell Story (2014) +4198,0.5,1,War Room (2015) +4199,3.5,2,Legend (2015) +4200,5.0,1,Deathgasm (2015) +4201,3.75,2,The Danish Girl (2015) +4202,3.5,1,Cooties (2015) +4203,4.0,1,Steve Jobs: The Man in the Machine (2015) +4204,3.3333333333333335,3,Green Room (2015) +4205,3.0,2,Beasts of No Nation (2015) +4206,5.0,1,Bloodsucking Bastards (2015) +4207,0.5,1,Saving Christmas (2014) +4208,3.0,1,Iron Man & Hulk: Heroes United (2013) +4209,1.5,1,Knock Knock (2015) +4210,4.75,2,The Blue Planet (2001) +4211,4.5,1,Cornered! (2009) +4212,3.125,4,Demolition (2016) +4213,3.0,1,Cigarette Burns (2005) +4214,2.75,2,Our Brand Is Crisis (2015) +4215,3.0,2,High Rise (2015) +4216,4.25,4,The Night Before (2015) +4217,2.0,1,Into the Forest (2015) +4218,5.0,1,The Editor (2015) +4219,3.0833333333333335,6,Everest (2015) +4220,4.0,2,The Brand New Testament (2015) +4221,4.157894736842105,19,Spotlight (2015) +4222,2.5,1,Pawn Sacrifice (2015) +4223,3.75,2,Hardcore Henry (2015) +4224,2.0,3,Burnt (2015) +4225,4.5,1,Ryuzo and the Seven Henchmen (2015) +4226,3.0,1,If I Were a Rich Man (2002) +4227,4.0,1,Last Shift (2014) +4228,1.0,1,"F*ck You, Goethe 2 (2015)" +4229,4.5,1,Garam Masala (2005) +4230,2.5,1,Life Eternal (2015) +4231,2.875,4,Hotel Transylvania 2 (2015) +4232,1.5,1,Anti-Social (2015) +4233,5.0,1,Jump In! (2007) +4234,2.25,2,The Little Prince (2015) +4235,1.5,1,Narcopolis (2014) +4236,3.0,2,Ashby (2015) +4237,3.3076923076923075,13,Wonder Woman (2017) +4238,2.5,1,The Circle (2016) +4239,3.75,2,Silence (2016) +4240,3.4375,8,Bridge of Spies (2015) +4241,4.0,1,The Great Hypnotist (2014) +4242,1.5,1,Into the Grizzly Maze (2015) +4243,5.0,1,Human (2015) +4244,4.0,1,Chasuke's Journey (2015) +4245,5.0,1,L.A. Slasher (2015) +4246,2.8125,8,"Hail, Caesar! (2016)" +4247,4.5,1,How To Change The World (2015) +4248,2.8333333333333335,3,Er ist wieder da (2015) +4249,4.0,1,Just Eat It: A Food Waste Story (2014) +4250,3.0,1,Bros Before Hos (2013) +4251,2.0,1,Slow Learners (2015) +4252,0.5,1,Unforgiven (2013) +4253,4.5,1,"Sex, Drugs & Taxation (2013)" +4254,2.0,1,Sky High (2003) +4255,4.0,1,Confessions of a Dangerous Mind (2002) +4256,2.0,2,Goosebumps (2015) +4257,1.5,1,The Perfect Guy (2015) +4258,2.0,1,Rock the Kasbah (2015) +4259,4.5,1,Freaks of Nature (2015) +4260,4.5,2,Bone Tomahawk (2015) +4261,2.5,1,Extraordinary Tales (2015) +4262,3.0,1,The Dressmaker (2015) +4263,3.5,1,Nowitzki: The Perfect Shot (2014) +4264,4.0,1,Trumbo (2015) +4265,3.5,1,Our Lips Are Sealed (2000) +4266,2.5,3,Concussion (2015) +4267,1.5,1,"Peanuts Movie, The (2015)" +4268,3.0,1,Blue Mountain State: The Rise of Thadland (2015) +4269,4.0,1,The Boy and the Beast (2015) +4270,3.75,8,Creed (2015) +4271,5.0,1,Dragons: Gift of the Night Fury (2011) +4272,4.0,1,Twinsters (2015) +4273,5.0,1,Cosmic Scrat-tastrophe (2015) +4274,2.0,1,Solace (2015) +4275,2.0,1,Lost in the Sun (2015) +4276,4.0,1,Eros (2004) +4277,3.0,1,Those Happy Days (2006) +4278,4.0,1,What Men Still Talk About (2011) +4279,3.75,2,Doctor Who: Last Christmas (2014) +4280,4.166666666666667,3,"Doctor Who: The Doctor, the Widow and the Wardrobe (2011)" +4281,4.75,2,Doctor Who: A Christmas Carol (2010) +4282,3.0,4,Doctor Who: Planet of the Dead (2009) +4283,2.875,4,Doctor Who: The Next Doctor (2008) +4284,4.5,3,Doctor Who: Voyage Of The Damned (2007) +4285,4.0,4,Doctor Who: The Runaway Bride (2007) +4286,5.0,1,A Perfect Day (2015) +4287,3.5,1,Hitchcock/Truffaut (2015) +4288,2.5,2,The 5th Wave (2016) +4289,2.5,1,A Very Murray Christmas (2015) +4290,3.75,2,Chi-Raq (2015) +4291,3.0,1,Truth (2015) +4292,3.0,1,Just Jim (2015) +4293,3.9615384615384617,26,"Big Short, The (2015)" +4294,4.5,1,Applesauce (2015) +4295,3.25,2,The Ridiculous 6 (2015) +4296,3.5,1,John Mulaney: The Comeback Kid (2015) +4297,4.5,2,Saw (2003) +4298,2.0,1,North Pole: Open For Christmas (2015) +4299,2.0,1,Mojave (2015) +4300,4.0,1,Wizards of Waverly Place: The Movie (2009) +4301,4.5,2,World of Tomorrow (2015) +4302,2.5,3,Zoolander 2 (2016) +4303,3.125,4,How to Be Single (2016) +4304,4.0,1,Blue Exorcist: The Movie (2012) +4305,3.5,1,He Never Died (2015) +4306,4.0,1,Parasyte: Part 1 (2014) +4307,4.0,1,Parasyte: Part 2 (2015) +4308,5.0,1,Lumberjack Man (2015) +4309,2.6666666666666665,6,Daddy's Home (2015) +4310,2.5,7,Sisters (2015) +4311,1.5,1,'Tis the Season for Love (2015) +4312,3.375,8,Kung Fu Panda 3 (2016) +4313,5.0,1,Spellbound (2011) +4314,5.0,1,Unicorn City (2012) +4315,2.5,1,Standoff (2016) +4316,2.0,1,Swelter (2014) +4317,4.5,1,Pride and Prejudice and Zombies (2016) +4318,2.0,1,Garm Wars: The Last Druid (2014) +4319,2.0,1,The Devil's Candy (2015) +4320,3.0,2,Close Range (2015) +4321,3.85,10,Sherlock: The Abominable Bride (2016) +4322,4.25,2,Doctor Who: The Husbands of River Song (2015) +4323,2.5,1,Moonwalkers (2015) +4324,4.5,1,Tomorrow (2015) +4325,4.0,1,Anacleto: Agente secreto (2015) +4326,3.0,1,Wiener-Dog (2016) +4327,2.0,3,Ride Along 2 (2016) +4328,2.0,1,Maggie's Plan (2015) +4329,2.8333333333333335,3,Eddie the Eagle (2016) +4330,1.5,1,Exposed (2016) +4331,2.5,1,Stonewall (2015) +4332,2.0,1,Frankenstein (2015) +4333,1.5,1,Welcome to Happiness (2015) +4334,0.5,1,Risen (2016) +4335,4.0,1,The Survivalist (2015) +4336,2.875,4,Dirty Grandpa (2016) +4337,2.5,1,Requiem for the American Dream (2015) +4338,4.0,2,Death Note Rewrite: Genshisuru Kami (2007) +4339,3.0,1,The Finest Hours (2016) +4340,3.5,1,Ghost in the Shell: Solid State Society (2006) +4341,2.0,2,Grease Live (2016) +4342,0.5,1,Gods of Egypt (2016) +4343,3.5,1,Embrace of the Serpent (2016) +4344,1.5,1,Race (2016) +4345,3.6785714285714284,14,10 Cloverfield Lane (2016) +4346,2.0,2,London Has Fallen (2016) +4347,3.890625,32,Zootopia (2016) +4348,3.3333333333333335,3,Whiskey Tango Foxtrot (2016) +4349,2.5,1,Desierto (2016) +4350,3.125,4,The Brothers Grimsby (2016) +4351,3.5,1,The Wait (2015) +4352,3.5,1,War and Peace (2016) +4353,3.5,1,Southbound (2016) +4354,4.0,1,Ip Man 3 (2015) +4355,1.5,1,Santa's Little Helper (2015) +4356,5.0,1,Who Killed Chea Vichea? (2010) +4357,4.375,4,Hunt for the Wilderpeople (2016) +4358,3.5,1,Rabbits (2002) +4359,3.0,1,Genius Party (2007) +4360,4.0,1,Tears for Sale (2008) +4361,2.0,1,Dad's Army (2016) +4362,4.0,1,The Barkley Marathons: The Race That Eats Its Young (2015) +4363,4.0,1,Merci Patron ! (2016) +4364,3.0,2,The Neon Demon (2016) +4365,4.0,1,Fraktus (2012) +4366,2.8333333333333335,3,Eye in the Sky (2016) +4367,2.0,1,Camino (2016) +4368,3.5,3,Mr. Right (2016) +4369,4.0,1,Florence Foster Jenkins (2016) +4370,2.0,1,My Big Fat Greek Wedding 2 (2016) +4371,3.5,1,Neon Bull (2015) +4372,3.25,2,Get a Job (2016) +4373,2.5,2,Keanu (2016) +4374,2.0,1,Me Him Her (2015) +4375,5.0,1,Ice Age: The Great Egg-Scapade (2016) +4376,4.166666666666667,3,Everybody Wants Some (2016) +4377,3.8333333333333335,3,Sing Street (2016) +4378,2.5,1,Zoom (2015) +4379,1.0,3,The Huntsman Winter's War (2016) +4380,2.6,5,Neighbors 2: Sorority Rising (2016) +4381,2.5,1,The Trust (2016) +4382,2.6666666666666665,3,Hush (2016) +4383,4.0,1,Jimmy Carr: Telling Jokes (2009) +4384,2.5,1,Jimmy Carr: Making People Laugh (2010) +4385,3.0,1,The Man Who Knew Infinity (2016) +4386,2.0,1,Despite the Falling Snow (2016) +4387,2.25,2,Money Monster (2016) +4388,2.5,1,Barbershop: The Next Cut (2016) +4389,3.576923076923077,13,Finding Dory (2016) +4390,2.6666666666666665,3,The Boss (2016) +4391,2.5,1,The Angry Birds Movie (2016) +4392,4.0,1,Bakuman (2015) +4393,1.5,1,I Am Wrath (2016) +4394,1.0,1,Precious Cargo (2016) +4395,3.8333333333333335,6,Snowden (2016) +4396,1.5,1,Ratchet & Clank (2016) +4397,1.5,1,Kicking Off (2016) +4398,5.0,1,SORI: Voice from the Heart (2016) +4399,3.5,1,Gintama: The Final Chapter - Be Forever Yorozuya (2013) +4400,3.8333333333333335,15,The Nice Guys (2016) +4401,1.5,1,Kindergarten Cop 2 (2016) +4402,2.5,1,The Crew (2016) +4403,2.5,1,The Shallows (2016) +4404,2.6666666666666665,3,The Handmaiden (2016) +4405,2.0,4,Alice Through the Looking Glass (2016) +4406,2.5,1,The BFG (2016) +4407,3.5,1,My Scientology Movie (2016) +4408,3.642857142857143,7,Sausage Party (2016) +4409,5.0,1,All Yours (2016) +4410,2.5,2,Kill Command (2016) +4411,4.5,5,Captain Fantastic (2016) +4412,3.6666666666666665,3,Toni Erdmann (2016) +4413,3.0,1,The Wailing (2016) +4414,2.5,1,The Meddler (2016) +4415,3.6363636363636362,11,Now You See Me 2 (2016) +4416,4.5,1,Ali Wong: Baby Cobra (2016) +4417,3.0,1,Café Society (2016) +4418,3.25,2,Swiss Army Man (2016) +4419,3.5,2,The Do-Over (2016) +4420,2.0,1,Teenage Mutant Ninja Turtles: Out of the Shadows (2016) +4421,3.75,2,The Fundamentals of Caring (2016) +4422,2.75,2,Popstar: Never Stop Never Stopping (2016) +4423,4.0,1,A Midsummer Night's Dream (2016) +4424,4.214285714285714,7,Planet Earth (2006) +4425,2.5,1,Bo Burnham: Make Happy (2016) +4426,2.25,2,The Conjuring 2 (2016) +4427,4.5,1,Pelé: Birth of a Legend (2016) +4428,3.125,8,Ghostbusters (2016) +4429,3.625,4,Central Intelligence (2016) +4430,4.5,1,O.J.: Made in America (2016) +4431,3.0,1,Genius (2016) +4432,2.7222222222222223,9,Jason Bourne (2016) +4433,1.5,1,The Maid's Room (2014) +4434,3.0833333333333335,6,The Legend of Tarzan (2016) +4435,2.75,2,The Purge: Election Year (2016) +4436,3.3333333333333335,3,Mike & Dave Need Wedding Dates (2016) +4437,1.0,1,Ice Age: Collision Course (2016) +4438,3.0,2,Lights Out (2016) +4439,3.0,1,Pete's Dragon (2016) +4440,5.0,1,Indignation (2016) +4441,2.5,1,Goat (2016) +4442,3.5,1,Marauders (2016) +4443,4.5,2,Piper (2016) +4444,2.0,1,The Adderall Diaries (2015) +4445,3.0,1,Hazard (2005) +4446,4.5,1,The Red Turtle (2016) +4447,0.5,1,Satanic (2016) +4448,2.625,4,Nerve (2016) +4449,1.0,1,Hellevator (2004) +4450,3.1666666666666665,6,Sully (2016) +4451,4.5,1,Jim Jefferies: Freedumb (2016) +4452,3.75,2,The Infiltrator (2016) +4453,3.625,8,War Dogs (2016) +4454,1.0,1,Vigilante Diaries (2016) +4455,3.0,1,Batman: The Killing Joke (2016) +4456,3.8333333333333335,3,Bad Moms (2016) +4457,3.5625,8,Hell or High Water (2016) +4458,3.5,1,Kingsglaive: Final Fantasy XV (2016) +4459,3.3,5,Don't Breathe (2016) +4460,1.0,1,Body (2015) +4461,1.5,1,Sharknado 4: The 4th Awakens (2016) +4462,3.25,2,The Edge of Seventeen (2016) +4463,4.0,1,Elle (2016) +4464,3.5,1,Train to Busan (2016) +4465,5.0,1,Tom Segura: Mostly Stories (2016) +4466,3.5833333333333335,6,The Magnificent Seven (2016) +4467,4.0,3,Masterminds (2016) +4468,4.0,6,Kubo and the Two Strings (2016) +4469,1.0,1,Bridget Jones's Baby (2016) +4470,3.5,1,Deepwater Horizon (2016) +4471,3.0,3,Miss Peregrine's Home for Peculiar Children (2016) +4472,4.5,1,The Girl on the Train (2016) +4473,3.0,6,The Accountant (2016) +4474,2.5,1,Imperium (2016) +4475,4.0,1,Kizumonogatari Part 1: Tekketsu (2016) +4476,3.0,1,Steins;Gate the Movie: The Burden of Déjà vu (2013) +4477,4.0,1,Shin Godzilla (2016) +4478,4.0,3,Your Name. (2016) +4479,4.0,1,Comedy Central Roast of David Hasselhoff (2010) +4480,4.0,1,DC Super Hero Girls: Hero of the Year (2016) +4481,3.9375,8,Hacksaw Ridge (2016) +4482,3.0,1,David Cross: Making America Great Again (2016) +4483,2.5,1,Over the Garden Wall (2013) +4484,3.5,1,Blair Witch (2016) +4485,3.5,1,31 (2016) +4486,4.5,1,ARQ (2016) +4487,3.980769230769231,26,Arrival (2016) +4488,4.0,2,Storks (2016) +4489,4.5,1,Maximum Ride (2016) +4490,3.5,1,Endless Poetry (2016) +4491,5.0,1,The Girl with All the Gifts (2016) +4492,1.5,1,All Roads Lead to Rome (2016) +4493,3.5,1,Amanda Knox (2016) +4494,2.5,1,Dirty 30 (2016) +4495,2.0,1,Gimme Danger (2016) +4496,4.0,1,Go Figure (2005) +4497,2.0,1,Anything for Love (2016) +4498,1.5,1,Night Guards (2016) +4499,3.388888888888889,9,La La Land (2016) +4500,4.5,2,13th (2016) +4501,2.5,1,London Town (2016) +4502,2.875,4,Inferno (2016) +4503,3.75,2,Keeping Up with the Joneses (2016) +4504,2.0,1,Wild Oats (2016) +4505,1.5,1,The Rocky Horror Picture Show: Let's Do the Time Warp Again (2016) +4506,2.0,1,Jack Reacher: Never Go Back (2016) +4507,4.0,1,Joe Rogan: Triggered (2016) +4508,2.5,1,Ethel & Ernest (2016) +4509,2.0,1,Flowers for Algernon (2000) +4510,2.9,5,Manchester by the Sea (2016) +4511,4.5,1,Lion (2016) +4512,1.0,1,The Thinning (2016) +4513,3.8333333333333335,3,While You Were Fighting: A Thor Mockumentary (2016) +4514,0.5,1,Bad Santa 2 (2016) +4515,4.5,1,Ice Guardians (2016) +4516,4.0,1,Risk (2016) +4517,3.25,2,The True Memoirs of an International Assassin (2016) +4518,5.0,1,Alesha Popovich and Tugarin the Dragon (2004) +4519,4.0,2,HyperNormalisation (2016) +4520,3.5,1,The African Doctor (2016) +4521,4.75,2,Whiplash (2013) +4522,4.0,1,Sapphire Blue (2014) +4523,4.0,2,A Silent Voice (2016) +4524,3.45,10,Moana (2016) +4525,3.5,3,Office Christmas Party (2016) +4526,1.0,1,The Space Between Us (2016) +4527,3.925925925925926,27,Rogue One: A Star Wars Story (2016) +4528,3.3333333333333335,6,Split (2017) +4529,3.0,1,Underworld: Blood Wars (2016) +4530,4.5,2,Miss Sloane (2016) +4531,3.6666666666666665,6,Passengers (2016) +4532,3.8,10,Hidden Figures (2016) +4533,3.0,1,Fences (2016) +4534,4.25,2,The Founder (2016) +4535,3.125,4,Why Him? (2016) +4536,3.75,4,Sing (2016) +4537,5.0,1,I Am Not Your Negro (2017) +4538,2.5,2,Assassin's Creed (2016) +4539,4.5,1,A Dog's Purpose (2017) +4540,4.5,1,Fist Fight (2017) +4541,2.5,1,Shakespeare Behind Bars (2005) +4542,3.5,1,A Street Cat Named Bob (2016) +4543,3.7142857142857144,7,The Lego Batman Movie (2017) +4544,3.5,1,The Good Boy (2016) +4545,4.5,1,"Dana Carvey: Straight White Male, 60 (2016)" +4546,4.0,1,Marvel One-Shot: Agent Carter (2013) +4547,4.0,1,Kizumonogatari II: Passionate Blood (2016) +4548,4.0,1,Joe Rogan: Live (2006) +4549,3.0,1,Jim Gaffigan: Cinco (2017) +4550,4.5,1,Kizumonogatari III: Cold Blood (2017) +4551,4.142857142857143,7,John Wick: Chapter Two (2017) +4552,3.6333333333333333,15,Get Out (2017) +4553,4.28,25,Logan (2017) +4554,3.125,4,Kong: Skull Island (2017) +4555,3.75,4,T2: Trainspotting (2017) +4556,4.0,4,The Big Sick (2017) +4557,2.5,1,100 Streets (2016) +4558,3.5,2,Beauty and the Beast (2017) +4559,3.8,5,The Boss Baby (2017) +4560,2.0,1,Mercury Plains (2016) +4561,3.75,4,Call Me by Your Name (2017) +4562,3.0,1,Mudbound (2017) +4563,3.75,2,Ghost in the Shell (2017) +4564,4.0,1,Bill Burr: Walk Your Way Out (2017) +4565,1.0,1,Fifty Shades Darker (2017) +4566,4.5,1,Neal Brennan: 3 Mics (2017) +4567,4.5,1,Lemonade (2016) +4568,2.5,1,American Fable (2017) +4569,1.5,1,The Void (2016) +4570,1.0,1,Buster's Mal Heart (2017) +4571,3.0,1,Power Rangers (2017) +4572,2.8,5,Alien: Covenant (2017) +4573,3.5,1,Free Fire (2017) +4574,1.0,1,Species III (2004) +4575,2.0,1,Ultimate Avengers 2 (2006) +4576,4.0,1,Dave Chappelle: The Age of Spin (2017) +4577,4.0,1,CHiPS (2017) +4578,3.0,1,Table 19 (2017) +4579,4.0,1,Dave Chappelle: Deep in the Heart of Texas (2017) +4580,4.125,4,Gifted (2017) +4581,4.285714285714286,7,Band of Brothers (2001) +4582,3.0,3,Baywatch (2017) +4583,4.0,1,Snatched (2017) +4584,3.5,1,The Mummy (2017) +4585,4.0,1,Life-Size (2000) +4586,2.3333333333333335,3,The Fate of the Furious (2017) +4587,2.0,1,Sandy Wexler (2017) +4588,4.0,1,Betting on Zero (2016) +4589,3.0,1,Win It All (2017) +4590,3.5,1,Captain Underpants: The First Epic Movie (2017) +4591,3.5,1,It Comes at Night (2017) +4592,3.0,1,Cars 3 (2017) +4593,4.0,1,Mini's First Time (2006) +4594,4.2,5,Planet Earth II (2016) +4595,4.0,1,The Hero (2017) +4596,4.0,1,"Nobody Speak: Hulk Hogan, Gawker and Trials of a Free Press (2017)" +4597,3.0,1,Robin Williams: Live on Broadway (2002) +4598,3.5,1,The Death of Louis XIV (2016) +4599,2.5,1,Munna bhai M.B.B.S. 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Denver Fenton Allen (2016) +4648,3.0,1,A German Life (2016) +4649,3.0,1,Self-criticism of a Bourgeois Dog (2017) +4650,3.5,1,LEGO DC Super Hero Girls: Brain Drain (2017) +4651,3.0625,8,Kingsman: The Golden Circle (2017) +4652,4.0,1,Ari Shaffir: Double Negative (2017) +4653,3.8055555555555554,18,Blade Runner 2049 (2017) +4654,2.0,1,The Nut Job 2: Nutty by Nature (2017) +4655,4.5,1,Bliss (2012) +4656,3.5,1,Alles Inklusive (2014) +4657,3.25,2,Mother! 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differ diff --git a/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/public_tests.py b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/public_tests.py new file mode 100644 index 00000000..68e33b68 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/public_tests.py @@ -0,0 +1,78 @@ +import numpy as np + +def test_cofi_cost_func(target): + num_users_r = 4 + num_movies_r = 5 + num_features_r = 3 + + X_r = np.ones((num_movies_r, num_features_r)) + W_r = np.ones((num_users_r, num_features_r)) + b_r = np.zeros((1, num_users_r)) + Y_r = np.zeros((num_movies_r, num_users_r)) + R_r = np.zeros((num_movies_r, num_users_r)) + + J = target(X_r, W_r, b_r, Y_r, R_r, 2); + assert not np.isclose(J, 13.5), f"Wrong value. Got {J}. Did you multiplied the regulartization term by lambda_?" + assert np.isclose(J, 27), f"Wrong value. Expected {27}, got {J}. Check the regularization term" + + + X_r = np.ones((num_movies_r, num_features_r)) + W_r = np.ones((num_users_r, num_features_r)) + b_r = np.ones((1, num_users_r)) + Y_r = np.ones((num_movies_r, num_users_r)) + R_r = np.ones((num_movies_r, num_users_r)) + + # Evaluate cost function + J = target(X_r, W_r, b_r, Y_r, R_r, 0); + + assert np.isclose(J, 90), f"Wrong value. Expected {90}, got {J}. Check the term without the regularization" + + + X_r = np.ones((num_movies_r, num_features_r)) + W_r = np.ones((num_users_r, num_features_r)) + b_r = np.ones((1, num_users_r)) + Y_r = np.zeros((num_movies_r, num_users_r)) + R_r = np.ones((num_movies_r, num_users_r)) + + # Evaluate cost function + J = target(X_r, W_r, b_r, Y_r, R_r, 0); + + assert np.isclose(J, 160), f"Wrong value. Expected {160}, got {J}. Check the term without the regularization" + + X_r = np.ones((num_movies_r, num_features_r)) + W_r = np.ones((num_users_r, num_features_r)) + b_r = np.ones((1, num_users_r)) + Y_r = np.ones((num_movies_r, num_users_r)) + R_r = np.ones((num_movies_r, num_users_r)) + + # Evaluate cost function + J = target(X_r, W_r, b_r, Y_r, R_r, 1); + + assert np.isclose(J, 103.5), f"Wrong value. Expected {103.5}, got {J}. Check the term without the regularization" + + num_users_r = 3 + num_movies_r = 4 + num_features_r = 4 + + #np.random.seed(247) + X_r = np.array([[0.36618032, 0.9075415, 0.8310605, 0.08590986], + [0.62634721, 0.38234325, 0.85624346, 0.55183039], + [0.77458727, 0.35704147, 0.31003294, 0.20100006], + [0.34420469, 0.46103436, 0.88638208, 0.36175401]])#np.random.rand(num_movies_r, num_features_r) + W_r = np.array([[0.04786854, 0.61504665, 0.06633146, 0.38298908], + [0.16515965, 0.22320207, 0.89826005, 0.14373251], + [0.1274051 , 0.22757303, 0.96865613, 0.70741111]])#np.random.rand(num_users_r, num_features_r) + b_r = np.array([[0.14246472, 0.30110933, 0.56141144]])#np.random.rand(1, num_users_r) + Y_r = np.array([[0.20651685, 0.60767914, 0.86344527], + [0.82665019, 0.00944765, 0.4376798 ], + [0.81623732, 0.26776794, 0.03757507], + [0.37232161, 0.19890823, 0.13026598]])#np.random.rand(num_movies_r, num_users_r) + R_r = np.array([[1, 0, 1], [1, 0, 0], [1, 0, 0], [0, 1, 0]])#(np.random.rand(num_movies_r, num_users_r) > 0.4) * 1 + + # Evaluate cost function + J = target(X_r, W_r, b_r, Y_r, R_r, 3); + + assert np.isclose(J, 13.621929978531858, atol=1e-8), f"Wrong value. Expected {13.621929978531858}, got {J}." + + print('\033[92mAll tests passed!') + diff --git a/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/recsys_utils.py b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/recsys_utils.py new file mode 100644 index 00000000..7ceff2a4 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/2 Practice Lab 1/recsys_utils.py @@ -0,0 +1,49 @@ +import numpy as np +import pandas as pd +from numpy import loadtxt + +def normalizeRatings(Y, R): + """ + Preprocess data by subtracting mean rating for every movie (every row). + Only include real ratings R(i,j)=1. + [Ynorm, Ymean] = normalizeRatings(Y, R) normalized Y so that each movie + has a rating of 0 on average. Unrated moves then have a mean rating (0) + Returns the mean rating in Ymean. + """ + Ymean = (np.sum(Y*R,axis=1)/(np.sum(R, axis=1)+1e-12)).reshape(-1,1) + Ynorm = Y - np.multiply(Ymean, R) + return(Ynorm, Ymean) + +def load_precalc_params_small(): + + file = open('./data/small_movies_X.csv', 'rb') + X = loadtxt(file, delimiter = ",") + + file = open('./data/small_movies_W.csv', 'rb') + W = loadtxt(file,delimiter = ",") + + file = open('./data/small_movies_b.csv', 'rb') + b = loadtxt(file,delimiter = ",") + b = b.reshape(1,-1) + num_movies, num_features = X.shape + num_users,_ = W.shape + return(X, W, b, num_movies, num_features, num_users) + +def load_ratings_small(): + file = open('./data/small_movies_Y.csv', 'rb') + Y = loadtxt(file,delimiter = ",") + + file = open('./data/small_movies_R.csv', 'rb') + R = loadtxt(file,delimiter = ",") + return(Y,R) + +def load_Movie_List_pd(): + """ returns df with and index of movies in the order they are in in the Y matrix """ + df = pd.read_csv('./data/small_movie_list.csv', header=0, index_col=0, delimiter=',', quotechar='"') + mlist = df["title"].to_list() + return(mlist, df) + + + + + diff --git a/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/.ipynb_checkpoints/C3_W2_RecSysNN_Assignment-checkpoint.ipynb b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/.ipynb_checkpoints/C3_W2_RecSysNN_Assignment-checkpoint.ipynb new file mode 100644 index 00000000..3ad334a0 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/.ipynb_checkpoints/C3_W2_RecSysNN_Assignment-checkpoint.ipynb @@ -0,0 +1,745 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Lzk7iX_CodX6", + "tags": [] + }, + "source": [ + "# Practice lab: Deep Learning for Content-Based Filtering\n", + "\n", + "In this exercise, you will implement content-based filtering using a neural network to build a recommender system for movies. \n", + "\n", + "# Outline \n", + "- [ 1 - Packages](#1)\n", + "- [ 2 - Movie ratings dataset](#2)\n", + " - [ 2.1 Content-based filtering with a neural network](#2.1)\n", + " - [ 2.2 Preparing the training data](#2.2)\n", + "- [ 3 - Neural Network for content-based filtering](#3)\n", + " - [ 3.1 Predictions](#3.1)\n", + " - [ Exercise 1](#ex01)\n", + "- [ 4 - Congratulations!](#4)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 - Packages \n", + "We will use familiar packages, NumPy, TensorFlow and helpful routines from [scikit-learn](https://scikit-learn.org/stable/). We will also use [tabulate](https://pypi.org/project/tabulate/) to neatly print tables and [Pandas](https://pandas.pydata.org/) to organize tabular data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xu-w_RmNwCV5" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy.ma as ma\n", + "from numpy import genfromtxt\n", + "from collections import defaultdict\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", + "from sklearn.model_selection import train_test_split\n", + "import tabulate\n", + "from recsysNN_utils import *\n", + "pd.set_option(\"display.precision\", 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2 - Movie ratings dataset \n", + "The data set is derived from the [MovieLens ml-latest-small](https://grouplens.org/datasets/movielens/latest/) dataset. \n", + "\n", + "[F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. ]\n", + "\n", + "The original dataset has 9000 movies rated by 600 users with ratings on a scale of 0.5 to 5 in 0.5 step increments. The dataset has been reduced in size to focus on movies from the years since 2000 and popular genres. The reduced dataset has $n_u = 395$ users and $n_m= 694$ movies. For each movie, the dataset provides a movie title, release date, and one or more genres. For example \"Toy Story 3\" was released in 2010 and has several genres: \"Adventure|Animation|Children|Comedy|Fantasy|IMAX\". This dataset contains little information about users other than their ratings. This dataset is used to create training vectors for the neural networks described below. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.1 Content-based filtering with a neural network\n", + "\n", + "In the collaborative filtering lab, you generated two vectors, a user vector and an item/movie vector whose dot product would predict a rating. The vectors were derived solely from the ratings. \n", + "\n", + "Content-based filtering also generates a user and movie feature vector but recognizes there may be other information available about the user and/or movie that may improve the prediction. The additional information is provided to a neural network which then generates the user and movie vector as shown below.\n", + "
\n", + "
\n", + "
\n", + "The movie content provided to the network is a combination of the original data and some 'engineered features'. Recall the feature engineering discussion and lab from Course 1, Week 2, lab 4. The original features are the year the movie was released and the movie's genre presented as a one-hot vector. There are 14 genres. The engineered feature is an average rating derived from the user ratings. Movies with multiple genre have a training vector per genre. \n", + "\n", + "The user content is composed of only engineered features. A per genre average rating is computed per user. Additionally, a user id, rating count and rating average are available, but are not included in the training or prediction content. They are useful in interpreting data.\n", + "\n", + "The training set consists of all the ratings made by the users in the data set. The user and movie/item vectors are presented to the above network together as a training set. The user vector is the same for all the movies rated by the user. \n", + "\n", + "Below, let's load and display some of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "M5gfMLYgxCD1" + }, + "outputs": [], + "source": [ + "# Load Data, set configuration variables\n", + "item_train, user_train, y_train, item_features, user_features, item_vecs, movie_dict, user_to_genre = load_data()\n", + "\n", + "num_user_features = user_train.shape[1] - 3 # remove userid, rating count and ave rating during training\n", + "num_item_features = item_train.shape[1] - 1 # remove movie id at train time\n", + "uvs = 3 # user genre vector start\n", + "ivs = 3 # item genre vector start\n", + "u_s = 3 # start of columns to use in training, user\n", + "i_s = 1 # start of columns to use in training, items\n", + "scaledata = True # applies the standard scalar to data if true\n", + "print(f\"Number of training vectors: {len(item_train)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the user and item/movie features are not used in training. Below, the features in brackets \"[]\" such as the \"user id\", \"rating count\" and \"rating ave\" are not included when the model is trained and used. Note, the user vector is the same for all the movies rated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pprint_train(user_train, user_features, uvs, u_s, maxcount=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pprint_train(item_train, item_features, ivs, i_s, maxcount=5, user=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"y_train[:5]: {y_train[:5]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above, we can see that movie 6874 is an action movie released in 2003. User 2 rates action movies as 3.9 on average. Further, movie 6874 was also listed in the Crime and Thriller genre. MovieLens users gave the movie an average rating of 4. A training example consists of a row from both tables and a rating from y_train." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.2 Preparing the training data\n", + "Recall in Course 1, Week 2, you explored feature scaling as a means of improving convergence. We'll scale the input features using the [scikit learn StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). This was used in Course 1, Week 2, Lab 5. Below, the inverse_transform is also shown to produce the original inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# scale training data\n", + "if scaledata:\n", + " item_train_save = item_train\n", + " user_train_save = user_train\n", + "\n", + " scalerItem = StandardScaler()\n", + " scalerItem.fit(item_train)\n", + " item_train = scalerItem.transform(item_train)\n", + "\n", + " scalerUser = StandardScaler()\n", + " scalerUser.fit(user_train)\n", + " user_train = scalerUser.transform(user_train)\n", + "\n", + " print(np.allclose(item_train_save, scalerItem.inverse_transform(item_train)))\n", + " print(np.allclose(user_train_save, scalerUser.inverse_transform(user_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To allow us to evaluate the results, we will split the data into training and test sets as was discussed in Course 2, Week 3. Here we will use [sklean train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to split and shuffle the data. Note that setting the initial random state to the same value ensures item, user, and y are shuffled identically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "item_train, item_test = train_test_split(item_train, train_size=0.80, shuffle=True, random_state=1)\n", + "user_train, user_test = train_test_split(user_train, train_size=0.80, shuffle=True, random_state=1)\n", + "y_train, y_test = train_test_split(y_train, train_size=0.80, shuffle=True, random_state=1)\n", + "print(f\"movie/item training data shape: {item_train.shape}\")\n", + "print(f\"movie/item test data shape: {item_test.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scaled, shuffled data now has a mean of zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pprint_train(user_train, user_features, uvs, u_s, maxcount=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KeoAhs95LRop" + }, + "source": [ + "Scale the target ratings using a Min Max Scaler to scale the target to be between -1 and 1. We use scikit-learn because it has an inverse_transform. [scikit learn MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A8myXMxFC8lP" + }, + "outputs": [], + "source": [ + "scaler = MinMaxScaler((-1, 1))\n", + "scaler.fit(y_train.reshape(-1, 1))\n", + "ynorm_train = scaler.transform(y_train.reshape(-1, 1))\n", + "ynorm_test = scaler.transform(y_test.reshape(-1, 1))\n", + "print(ynorm_train.shape, ynorm_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 3 - Neural Network for content-based filtering\n", + "Now, let's construct a neural network as described in the figure above. It will have two networks that are combined by a dot product. You will construct the two networks. In this example, they will be identical. Note that these networks do not need to be the same. If the user content was substantially larger than the movie content, you might elect to increase the complexity of the user network relative to the movie network. In this case, the content is similar, so the networks are the same.\n", + "\n", + "- Use a Keras sequential model\n", + " - The first layer is a dense layer with 256 units and a relu activation.\n", + " - The second layer is a dense layer with 128 units and a relu activation.\n", + " - The third layer is a dense layer with `num_outputs` units and a linear or no activation. \n", + " \n", + "The remainder of the network will be provided. The provided code does not use the Keras sequential model but instead uses the Keras [functional api](https://keras.io/guides/functional_api/). This format allows for more flexibility in how components are interconnected.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CBjZ2HhRwpa0" + }, + "outputs": [], + "source": [ + "# GRADED_CELL\n", + "# UNQ_C1\n", + "\n", + "num_outputs = 32\n", + "tf.random.set_seed(1)\n", + "user_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " \n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "item_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + "\n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "# create the user input and point to the base network\n", + "input_user = tf.keras.layers.Input(shape=(num_user_features))\n", + "vu = user_NN(input_user)\n", + "vu = tf.linalg.l2_normalize(vu, axis=1)\n", + "\n", + "# create the item input and point to the base network\n", + "input_item = tf.keras.layers.Input(shape=(num_item_features))\n", + "vm = item_NN(input_item)\n", + "vm = tf.linalg.l2_normalize(vm, axis=1)\n", + "\n", + "# compute the dot product of the two vectors vu and vm\n", + "output = tf.keras.layers.Dot(axes=1)([vu, vm])\n", + "\n", + "# specify the inputs and output of the model\n", + "model = Model([input_user, input_item], output)\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Public tests\n", + "from public_tests import *\n", + "test_tower(user_NN)\n", + "test_tower(item_NN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + " You can create a dense layer with a relu activation as shown.\n", + " \n", + "```python \n", + "user_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + "\n", + " \n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "item_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + "\n", + " \n", + " ### END CODE HERE ### \n", + "])\n", + "``` \n", + "
\n", + " Click for solution\n", + " \n", + "```python \n", + "user_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(num_outputs),\n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "item_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(num_outputs),\n", + " ### END CODE HERE ### \n", + "])\n", + "```\n", + "
\n", + "
\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use a mean squared error loss and an Adam optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pGK5MEUowxN4" + }, + "outputs": [], + "source": [ + "tf.random.set_seed(1)\n", + "cost_fn = tf.keras.losses.MeanSquaredError()\n", + "opt = keras.optimizers.Adam(learning_rate=0.01)\n", + "model.compile(optimizer=opt,\n", + " loss=cost_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6zHf7eASw0tN" + }, + "outputs": [], + "source": [ + "tf.random.set_seed(1)\n", + "model.fit([user_train[:, u_s:], item_train[:, i_s:]], ynorm_train, epochs=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evaluate the model to determine loss on the test data. It is comparable to the training loss indicating the model has not substantially overfit the training data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.evaluate([user_test[:, u_s:], item_test[:, i_s:]], ynorm_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xsre-gquwEls" + }, + "source": [ + "\n", + "### 3.1 Predictions\n", + "Below, you'll use your model to make predictions in a number of circumstances. \n", + "#### Predictions for a new user\n", + "First, we'll create a new user and have the model suggest movies for that user. After you have tried this example on the example user content, feel free to change the user content to match your own preferences and see what the model suggests. Note that ratings are between 0.5 and 5.0, inclusive, in half-step increments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4_7nZyPiVJ4r" + }, + "outputs": [], + "source": [ + "new_user_id = 5000\n", + "new_rating_ave = 1.0\n", + "new_action = 1.0\n", + "new_adventure = 1\n", + "new_animation = 1\n", + "new_childrens = 1\n", + "new_comedy = 5\n", + "new_crime = 1\n", + "new_documentary = 1\n", + "new_drama = 1\n", + "new_fantasy = 1\n", + "new_horror = 1\n", + "new_mystery = 1\n", + "new_romance = 5\n", + "new_scifi = 5\n", + "new_thriller = 1\n", + "new_rating_count = 3\n", + "\n", + "user_vec = np.array([[new_user_id, new_rating_count, new_rating_ave,\n", + " new_action, new_adventure, new_animation, new_childrens,\n", + " new_comedy, new_crime, new_documentary,\n", + " new_drama, new_fantasy, new_horror, new_mystery,\n", + " new_romance, new_scifi, new_thriller]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Let's look at the top-rated movies for the new user. Recall, the user vector had genres that favored Comedy and Romance.\n", + "Below, we'll use a set of movie/item vectors, `item_vecs` that have a vector for each movie in the training/test set. This is matched with the user vector above and the scaled vectors are used to predict ratings for all the movies for our new user above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate and replicate the user vector to match the number movies in the data set.\n", + "user_vecs = gen_user_vecs(user_vec,len(item_vecs))\n", + "\n", + "# scale the vectors and make predictions for all movies. Return results sorted by rating.\n", + "sorted_index, sorted_ypu, sorted_items, sorted_user = predict_uservec(user_vecs, item_vecs, model, u_s, i_s, \n", + " scaler, scalerUser, scalerItem, scaledata=scaledata)\n", + "\n", + "print_pred_movies(sorted_ypu, sorted_user, sorted_items, movie_dict, maxcount = 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you do create a user above, it is worth noting that the network was trained to predict a user rating given a user vector that includes a **set** of user genre ratings. Simply providing a maximum rating for a single genre and minimum ratings for the rest may not be meaningful to the network if there were no users with similar sets of ratings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Predictions for an existing user.\n", + "Let's look at the predictions for \"user 36\", one of the users in the data set. We can compare the predicted ratings with the model's ratings. Note that movies with multiple genre's show up multiple times in the training data. For example,'The Time Machine' has three genre's: Adventure, Action, Sci-Fi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uid = 36 \n", + "# form a set of user vectors. This is the same vector, transformed and repeated.\n", + "user_vecs, y_vecs = get_user_vecs(uid, scalerUser.inverse_transform(user_train), item_vecs, user_to_genre)\n", + "\n", + "# scale the vectors and make predictions for all movies. Return results sorted by rating.\n", + "sorted_index, sorted_ypu, sorted_items, sorted_user = predict_uservec(user_vecs, item_vecs, model, u_s, i_s, scaler, \n", + " scalerUser, scalerItem, scaledata=scaledata)\n", + "sorted_y = y_vecs[sorted_index]\n", + "\n", + "#print sorted predictions\n", + "print_existing_user(sorted_ypu, sorted_y.reshape(-1,1), sorted_user, sorted_items, item_features, ivs, uvs, movie_dict, maxcount = 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Finding Similar Items\n", + "The neural network above produces two feature vectors, a user feature vector $v_u$, and a movie feature vector, $v_m$. These are 32 entry vectors whose values are difficult to interpret. However, similar items will have similar vectors. This information can be used to make recommendations. For example, if a user has rated \"Toy Story 3\" highly, one could recommend similar movies by selecting movies with similar movie feature vectors.\n", + "\n", + "A similarity measure is the squared distance between the two vectors $ \\mathbf{v_m^{(k)}}$ and $\\mathbf{v_m^{(i)}}$ :\n", + "$$\\left\\Vert \\mathbf{v_m^{(k)}} - \\mathbf{v_m^{(i)}} \\right\\Vert^2 = \\sum_{l=1}^{n}(v_{m_l}^{(k)} - v_{m_l}^{(i)})^2\\tag{1}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 1\n", + "\n", + "Write a function to compute the square distance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED_FUNCTION: sq_dist\n", + "# UNQ_C2\n", + "def sq_dist(a,b):\n", + " \"\"\"\n", + " Returns the squared distance between two vectors\n", + " Args:\n", + " a (ndarray (n,)): vector with n features\n", + " b (ndarray (n,)): vector with n features\n", + " Returns:\n", + " d (float) : distance\n", + " \"\"\"\n", + " ### START CODE HERE ### \n", + " \n", + " ### END CODE HERE ### \n", + " return (d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Public tests\n", + "test_sq_dist(sq_dist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a1 = np.array([1.0, 2.0, 3.0]); b1 = np.array([1.0, 2.0, 3.0])\n", + "a2 = np.array([1.1, 2.1, 3.1]); b2 = np.array([1.0, 2.0, 3.0])\n", + "a3 = np.array([0, 1, 0]); b3 = np.array([1, 0, 0])\n", + "print(f\"squared distance between a1 and b1: {sq_dist(a1, b1)}\")\n", + "print(f\"squared distance between a2 and b2: {sq_dist(a2, b2)}\")\n", + "print(f\"squared distance between a3 and b3: {sq_dist(a3, b3)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + " While a summation is often an indication a for loop should be used, here the subtraction can be element-wise in one statement. Further, you can utilized np.square to square, element-wise, the result of the subtraction. np.sum can be used to sum the squared elements.\n", + " \n", + "
\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A matrix of distances between movies can be computed once when the model is trained and then reused for new recommendations without retraining. The first step, once a model is trained, is to obtain the movie feature vector, $v_m$, for each of the movies. To do this, we will use the trained `item_NN` and build a small model to allow us to run the movie vectors through it to generate $v_m$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_item_m = tf.keras.layers.Input(shape=(num_item_features)) # input layer\n", + "vm_m = item_NN(input_item_m) # use the trained item_NN\n", + "vm_m = tf.linalg.l2_normalize(vm_m, axis=1) # incorporate normalization as was done in the original model\n", + "model_m = Model(input_item_m, vm_m) \n", + "model_m.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have a movie model, you can create a set of movie feature vectors by using the model to predict using a set of item/movie vectors as input. `item_vecs` is a set of all of the movie vectors. Recall that the same movie will appear as a separate vector for each of its genres. It must be scaled to use with the trained model. The result of the prediction is a 32 entry feature vector for each movie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scaled_item_vecs = scalerItem.transform(item_vecs)\n", + "vms = model_m.predict(scaled_item_vecs[:,i_s:])\n", + "print(f\"size of all predicted movie feature vectors: {vms.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now compute a matrix of the squared distance between each movie feature vector and all other movie feature vectors:\n", + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then find the closest movie by finding the minimum along each row. We will make use of [numpy masked arrays](https://numpy.org/doc/1.21/user/tutorial-ma.html) to avoid selecting the same movie. The masked values along the diagonal won't be included in the computation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "count = 50\n", + "dim = len(vms)\n", + "dist = np.zeros((dim,dim))\n", + "\n", + "for i in range(dim):\n", + " for j in range(dim):\n", + " dist[i,j] = sq_dist(vms[i, :], vms[j, :])\n", + " \n", + "m_dist = ma.masked_array(dist, mask=np.identity(dist.shape[0])) # mask the diagonal\n", + "\n", + "disp = [[\"movie1\", \"genres\", \"movie2\", \"genres\"]]\n", + "for i in range(count):\n", + " min_idx = np.argmin(m_dist[i])\n", + " movie1_id = int(item_vecs[i,0])\n", + " movie2_id = int(item_vecs[min_idx,0])\n", + " genre1,_ = get_item_genre(item_vecs[i,:], ivs, item_features)\n", + " genre2,_ = get_item_genre(item_vecs[min_idx,:], ivs, item_features)\n", + "\n", + " disp.append( [movie_dict[movie1_id]['title'], genre1,\n", + " movie_dict[movie2_id]['title'], genre2]\n", + " )\n", + "table = tabulate.tabulate(disp, tablefmt='html', headers=\"firstrow\", floatfmt=[\".1f\", \".1f\", \".0f\", \".2f\", \".2f\"])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results show the model will suggest a movie from the same genre." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4 - Congratulations! \n", + "You have completed a content-based recommender system. \n", + "\n", + "This structure is the basis of many commercial recommender systems. The user content can be greatly expanded to incorporate more information about the user if it is available. Items are not limited to movies. This can be used to recommend any item, books, cars or items that are similar to an item in your 'shopping cart'." + ] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyOFYdA6zQJ1FpgYwYmRIeXa", + "collapsed_sections": [], + "name": "Recsys_NN.ipynb", + "private_outputs": true, + "provenance": [ + { + "file_id": "1RO0HLb7kRE0Tj_0D4E5I-vQz2QLu3CUm", + "timestamp": 1655169179306 + } + ] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/C3_W2_RecSysNN_Assignment.ipynb b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/C3_W2_RecSysNN_Assignment.ipynb new file mode 100644 index 00000000..3ad334a0 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/C3_W2_RecSysNN_Assignment.ipynb @@ -0,0 +1,745 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Lzk7iX_CodX6", + "tags": [] + }, + "source": [ + "# Practice lab: Deep Learning for Content-Based Filtering\n", + "\n", + "In this exercise, you will implement content-based filtering using a neural network to build a recommender system for movies. \n", + "\n", + "# Outline \n", + "- [ 1 - Packages](#1)\n", + "- [ 2 - Movie ratings dataset](#2)\n", + " - [ 2.1 Content-based filtering with a neural network](#2.1)\n", + " - [ 2.2 Preparing the training data](#2.2)\n", + "- [ 3 - Neural Network for content-based filtering](#3)\n", + " - [ 3.1 Predictions](#3.1)\n", + " - [ Exercise 1](#ex01)\n", + "- [ 4 - Congratulations!](#4)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 - Packages \n", + "We will use familiar packages, NumPy, TensorFlow and helpful routines from [scikit-learn](https://scikit-learn.org/stable/). We will also use [tabulate](https://pypi.org/project/tabulate/) to neatly print tables and [Pandas](https://pandas.pydata.org/) to organize tabular data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xu-w_RmNwCV5" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy.ma as ma\n", + "from numpy import genfromtxt\n", + "from collections import defaultdict\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", + "from sklearn.model_selection import train_test_split\n", + "import tabulate\n", + "from recsysNN_utils import *\n", + "pd.set_option(\"display.precision\", 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2 - Movie ratings dataset \n", + "The data set is derived from the [MovieLens ml-latest-small](https://grouplens.org/datasets/movielens/latest/) dataset. \n", + "\n", + "[F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. ]\n", + "\n", + "The original dataset has 9000 movies rated by 600 users with ratings on a scale of 0.5 to 5 in 0.5 step increments. The dataset has been reduced in size to focus on movies from the years since 2000 and popular genres. The reduced dataset has $n_u = 395$ users and $n_m= 694$ movies. For each movie, the dataset provides a movie title, release date, and one or more genres. For example \"Toy Story 3\" was released in 2010 and has several genres: \"Adventure|Animation|Children|Comedy|Fantasy|IMAX\". This dataset contains little information about users other than their ratings. This dataset is used to create training vectors for the neural networks described below. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.1 Content-based filtering with a neural network\n", + "\n", + "In the collaborative filtering lab, you generated two vectors, a user vector and an item/movie vector whose dot product would predict a rating. The vectors were derived solely from the ratings. \n", + "\n", + "Content-based filtering also generates a user and movie feature vector but recognizes there may be other information available about the user and/or movie that may improve the prediction. The additional information is provided to a neural network which then generates the user and movie vector as shown below.\n", + "
\n", + "
\n", + "
\n", + "The movie content provided to the network is a combination of the original data and some 'engineered features'. Recall the feature engineering discussion and lab from Course 1, Week 2, lab 4. The original features are the year the movie was released and the movie's genre presented as a one-hot vector. There are 14 genres. The engineered feature is an average rating derived from the user ratings. Movies with multiple genre have a training vector per genre. \n", + "\n", + "The user content is composed of only engineered features. A per genre average rating is computed per user. Additionally, a user id, rating count and rating average are available, but are not included in the training or prediction content. They are useful in interpreting data.\n", + "\n", + "The training set consists of all the ratings made by the users in the data set. The user and movie/item vectors are presented to the above network together as a training set. The user vector is the same for all the movies rated by the user. \n", + "\n", + "Below, let's load and display some of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "M5gfMLYgxCD1" + }, + "outputs": [], + "source": [ + "# Load Data, set configuration variables\n", + "item_train, user_train, y_train, item_features, user_features, item_vecs, movie_dict, user_to_genre = load_data()\n", + "\n", + "num_user_features = user_train.shape[1] - 3 # remove userid, rating count and ave rating during training\n", + "num_item_features = item_train.shape[1] - 1 # remove movie id at train time\n", + "uvs = 3 # user genre vector start\n", + "ivs = 3 # item genre vector start\n", + "u_s = 3 # start of columns to use in training, user\n", + "i_s = 1 # start of columns to use in training, items\n", + "scaledata = True # applies the standard scalar to data if true\n", + "print(f\"Number of training vectors: {len(item_train)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the user and item/movie features are not used in training. Below, the features in brackets \"[]\" such as the \"user id\", \"rating count\" and \"rating ave\" are not included when the model is trained and used. Note, the user vector is the same for all the movies rated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pprint_train(user_train, user_features, uvs, u_s, maxcount=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pprint_train(item_train, item_features, ivs, i_s, maxcount=5, user=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"y_train[:5]: {y_train[:5]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above, we can see that movie 6874 is an action movie released in 2003. User 2 rates action movies as 3.9 on average. Further, movie 6874 was also listed in the Crime and Thriller genre. MovieLens users gave the movie an average rating of 4. A training example consists of a row from both tables and a rating from y_train." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 2.2 Preparing the training data\n", + "Recall in Course 1, Week 2, you explored feature scaling as a means of improving convergence. We'll scale the input features using the [scikit learn StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). This was used in Course 1, Week 2, Lab 5. Below, the inverse_transform is also shown to produce the original inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# scale training data\n", + "if scaledata:\n", + " item_train_save = item_train\n", + " user_train_save = user_train\n", + "\n", + " scalerItem = StandardScaler()\n", + " scalerItem.fit(item_train)\n", + " item_train = scalerItem.transform(item_train)\n", + "\n", + " scalerUser = StandardScaler()\n", + " scalerUser.fit(user_train)\n", + " user_train = scalerUser.transform(user_train)\n", + "\n", + " print(np.allclose(item_train_save, scalerItem.inverse_transform(item_train)))\n", + " print(np.allclose(user_train_save, scalerUser.inverse_transform(user_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To allow us to evaluate the results, we will split the data into training and test sets as was discussed in Course 2, Week 3. Here we will use [sklean train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to split and shuffle the data. Note that setting the initial random state to the same value ensures item, user, and y are shuffled identically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "item_train, item_test = train_test_split(item_train, train_size=0.80, shuffle=True, random_state=1)\n", + "user_train, user_test = train_test_split(user_train, train_size=0.80, shuffle=True, random_state=1)\n", + "y_train, y_test = train_test_split(y_train, train_size=0.80, shuffle=True, random_state=1)\n", + "print(f\"movie/item training data shape: {item_train.shape}\")\n", + "print(f\"movie/item test data shape: {item_test.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scaled, shuffled data now has a mean of zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pprint_train(user_train, user_features, uvs, u_s, maxcount=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KeoAhs95LRop" + }, + "source": [ + "Scale the target ratings using a Min Max Scaler to scale the target to be between -1 and 1. We use scikit-learn because it has an inverse_transform. [scikit learn MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A8myXMxFC8lP" + }, + "outputs": [], + "source": [ + "scaler = MinMaxScaler((-1, 1))\n", + "scaler.fit(y_train.reshape(-1, 1))\n", + "ynorm_train = scaler.transform(y_train.reshape(-1, 1))\n", + "ynorm_test = scaler.transform(y_test.reshape(-1, 1))\n", + "print(ynorm_train.shape, ynorm_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 3 - Neural Network for content-based filtering\n", + "Now, let's construct a neural network as described in the figure above. It will have two networks that are combined by a dot product. You will construct the two networks. In this example, they will be identical. Note that these networks do not need to be the same. If the user content was substantially larger than the movie content, you might elect to increase the complexity of the user network relative to the movie network. In this case, the content is similar, so the networks are the same.\n", + "\n", + "- Use a Keras sequential model\n", + " - The first layer is a dense layer with 256 units and a relu activation.\n", + " - The second layer is a dense layer with 128 units and a relu activation.\n", + " - The third layer is a dense layer with `num_outputs` units and a linear or no activation. \n", + " \n", + "The remainder of the network will be provided. The provided code does not use the Keras sequential model but instead uses the Keras [functional api](https://keras.io/guides/functional_api/). This format allows for more flexibility in how components are interconnected.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CBjZ2HhRwpa0" + }, + "outputs": [], + "source": [ + "# GRADED_CELL\n", + "# UNQ_C1\n", + "\n", + "num_outputs = 32\n", + "tf.random.set_seed(1)\n", + "user_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " \n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "item_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + "\n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "# create the user input and point to the base network\n", + "input_user = tf.keras.layers.Input(shape=(num_user_features))\n", + "vu = user_NN(input_user)\n", + "vu = tf.linalg.l2_normalize(vu, axis=1)\n", + "\n", + "# create the item input and point to the base network\n", + "input_item = tf.keras.layers.Input(shape=(num_item_features))\n", + "vm = item_NN(input_item)\n", + "vm = tf.linalg.l2_normalize(vm, axis=1)\n", + "\n", + "# compute the dot product of the two vectors vu and vm\n", + "output = tf.keras.layers.Dot(axes=1)([vu, vm])\n", + "\n", + "# specify the inputs and output of the model\n", + "model = Model([input_user, input_item], output)\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Public tests\n", + "from public_tests import *\n", + "test_tower(user_NN)\n", + "test_tower(item_NN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + " You can create a dense layer with a relu activation as shown.\n", + " \n", + "```python \n", + "user_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + "\n", + " \n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "item_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + "\n", + " \n", + " ### END CODE HERE ### \n", + "])\n", + "``` \n", + "
\n", + " Click for solution\n", + " \n", + "```python \n", + "user_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(num_outputs),\n", + " ### END CODE HERE ### \n", + "])\n", + "\n", + "item_NN = tf.keras.models.Sequential([\n", + " ### START CODE HERE ### \n", + " tf.keras.layers.Dense(256, activation='relu'),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(num_outputs),\n", + " ### END CODE HERE ### \n", + "])\n", + "```\n", + "
\n", + "
\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use a mean squared error loss and an Adam optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pGK5MEUowxN4" + }, + "outputs": [], + "source": [ + "tf.random.set_seed(1)\n", + "cost_fn = tf.keras.losses.MeanSquaredError()\n", + "opt = keras.optimizers.Adam(learning_rate=0.01)\n", + "model.compile(optimizer=opt,\n", + " loss=cost_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6zHf7eASw0tN" + }, + "outputs": [], + "source": [ + "tf.random.set_seed(1)\n", + "model.fit([user_train[:, u_s:], item_train[:, i_s:]], ynorm_train, epochs=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evaluate the model to determine loss on the test data. It is comparable to the training loss indicating the model has not substantially overfit the training data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.evaluate([user_test[:, u_s:], item_test[:, i_s:]], ynorm_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xsre-gquwEls" + }, + "source": [ + "\n", + "### 3.1 Predictions\n", + "Below, you'll use your model to make predictions in a number of circumstances. \n", + "#### Predictions for a new user\n", + "First, we'll create a new user and have the model suggest movies for that user. After you have tried this example on the example user content, feel free to change the user content to match your own preferences and see what the model suggests. Note that ratings are between 0.5 and 5.0, inclusive, in half-step increments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4_7nZyPiVJ4r" + }, + "outputs": [], + "source": [ + "new_user_id = 5000\n", + "new_rating_ave = 1.0\n", + "new_action = 1.0\n", + "new_adventure = 1\n", + "new_animation = 1\n", + "new_childrens = 1\n", + "new_comedy = 5\n", + "new_crime = 1\n", + "new_documentary = 1\n", + "new_drama = 1\n", + "new_fantasy = 1\n", + "new_horror = 1\n", + "new_mystery = 1\n", + "new_romance = 5\n", + "new_scifi = 5\n", + "new_thriller = 1\n", + "new_rating_count = 3\n", + "\n", + "user_vec = np.array([[new_user_id, new_rating_count, new_rating_ave,\n", + " new_action, new_adventure, new_animation, new_childrens,\n", + " new_comedy, new_crime, new_documentary,\n", + " new_drama, new_fantasy, new_horror, new_mystery,\n", + " new_romance, new_scifi, new_thriller]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Let's look at the top-rated movies for the new user. Recall, the user vector had genres that favored Comedy and Romance.\n", + "Below, we'll use a set of movie/item vectors, `item_vecs` that have a vector for each movie in the training/test set. This is matched with the user vector above and the scaled vectors are used to predict ratings for all the movies for our new user above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate and replicate the user vector to match the number movies in the data set.\n", + "user_vecs = gen_user_vecs(user_vec,len(item_vecs))\n", + "\n", + "# scale the vectors and make predictions for all movies. Return results sorted by rating.\n", + "sorted_index, sorted_ypu, sorted_items, sorted_user = predict_uservec(user_vecs, item_vecs, model, u_s, i_s, \n", + " scaler, scalerUser, scalerItem, scaledata=scaledata)\n", + "\n", + "print_pred_movies(sorted_ypu, sorted_user, sorted_items, movie_dict, maxcount = 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you do create a user above, it is worth noting that the network was trained to predict a user rating given a user vector that includes a **set** of user genre ratings. Simply providing a maximum rating for a single genre and minimum ratings for the rest may not be meaningful to the network if there were no users with similar sets of ratings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Predictions for an existing user.\n", + "Let's look at the predictions for \"user 36\", one of the users in the data set. We can compare the predicted ratings with the model's ratings. Note that movies with multiple genre's show up multiple times in the training data. For example,'The Time Machine' has three genre's: Adventure, Action, Sci-Fi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uid = 36 \n", + "# form a set of user vectors. This is the same vector, transformed and repeated.\n", + "user_vecs, y_vecs = get_user_vecs(uid, scalerUser.inverse_transform(user_train), item_vecs, user_to_genre)\n", + "\n", + "# scale the vectors and make predictions for all movies. Return results sorted by rating.\n", + "sorted_index, sorted_ypu, sorted_items, sorted_user = predict_uservec(user_vecs, item_vecs, model, u_s, i_s, scaler, \n", + " scalerUser, scalerItem, scaledata=scaledata)\n", + "sorted_y = y_vecs[sorted_index]\n", + "\n", + "#print sorted predictions\n", + "print_existing_user(sorted_ypu, sorted_y.reshape(-1,1), sorted_user, sorted_items, item_features, ivs, uvs, movie_dict, maxcount = 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Finding Similar Items\n", + "The neural network above produces two feature vectors, a user feature vector $v_u$, and a movie feature vector, $v_m$. These are 32 entry vectors whose values are difficult to interpret. However, similar items will have similar vectors. This information can be used to make recommendations. For example, if a user has rated \"Toy Story 3\" highly, one could recommend similar movies by selecting movies with similar movie feature vectors.\n", + "\n", + "A similarity measure is the squared distance between the two vectors $ \\mathbf{v_m^{(k)}}$ and $\\mathbf{v_m^{(i)}}$ :\n", + "$$\\left\\Vert \\mathbf{v_m^{(k)}} - \\mathbf{v_m^{(i)}} \\right\\Vert^2 = \\sum_{l=1}^{n}(v_{m_l}^{(k)} - v_{m_l}^{(i)})^2\\tag{1}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 1\n", + "\n", + "Write a function to compute the square distance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED_FUNCTION: sq_dist\n", + "# UNQ_C2\n", + "def sq_dist(a,b):\n", + " \"\"\"\n", + " Returns the squared distance between two vectors\n", + " Args:\n", + " a (ndarray (n,)): vector with n features\n", + " b (ndarray (n,)): vector with n features\n", + " Returns:\n", + " d (float) : distance\n", + " \"\"\"\n", + " ### START CODE HERE ### \n", + " \n", + " ### END CODE HERE ### \n", + " return (d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Public tests\n", + "test_sq_dist(sq_dist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a1 = np.array([1.0, 2.0, 3.0]); b1 = np.array([1.0, 2.0, 3.0])\n", + "a2 = np.array([1.1, 2.1, 3.1]); b2 = np.array([1.0, 2.0, 3.0])\n", + "a3 = np.array([0, 1, 0]); b3 = np.array([1, 0, 0])\n", + "print(f\"squared distance between a1 and b1: {sq_dist(a1, b1)}\")\n", + "print(f\"squared distance between a2 and b2: {sq_dist(a2, b2)}\")\n", + "print(f\"squared distance between a3 and b3: {sq_dist(a3, b3)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + " While a summation is often an indication a for loop should be used, here the subtraction can be element-wise in one statement. Further, you can utilized np.square to square, element-wise, the result of the subtraction. np.sum can be used to sum the squared elements.\n", + " \n", + "
\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A matrix of distances between movies can be computed once when the model is trained and then reused for new recommendations without retraining. The first step, once a model is trained, is to obtain the movie feature vector, $v_m$, for each of the movies. To do this, we will use the trained `item_NN` and build a small model to allow us to run the movie vectors through it to generate $v_m$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_item_m = tf.keras.layers.Input(shape=(num_item_features)) # input layer\n", + "vm_m = item_NN(input_item_m) # use the trained item_NN\n", + "vm_m = tf.linalg.l2_normalize(vm_m, axis=1) # incorporate normalization as was done in the original model\n", + "model_m = Model(input_item_m, vm_m) \n", + "model_m.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have a movie model, you can create a set of movie feature vectors by using the model to predict using a set of item/movie vectors as input. `item_vecs` is a set of all of the movie vectors. Recall that the same movie will appear as a separate vector for each of its genres. It must be scaled to use with the trained model. The result of the prediction is a 32 entry feature vector for each movie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scaled_item_vecs = scalerItem.transform(item_vecs)\n", + "vms = model_m.predict(scaled_item_vecs[:,i_s:])\n", + "print(f\"size of all predicted movie feature vectors: {vms.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now compute a matrix of the squared distance between each movie feature vector and all other movie feature vectors:\n", + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then find the closest movie by finding the minimum along each row. We will make use of [numpy masked arrays](https://numpy.org/doc/1.21/user/tutorial-ma.html) to avoid selecting the same movie. The masked values along the diagonal won't be included in the computation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "count = 50\n", + "dim = len(vms)\n", + "dist = np.zeros((dim,dim))\n", + "\n", + "for i in range(dim):\n", + " for j in range(dim):\n", + " dist[i,j] = sq_dist(vms[i, :], vms[j, :])\n", + " \n", + "m_dist = ma.masked_array(dist, mask=np.identity(dist.shape[0])) # mask the diagonal\n", + "\n", + "disp = [[\"movie1\", \"genres\", \"movie2\", \"genres\"]]\n", + "for i in range(count):\n", + " min_idx = np.argmin(m_dist[i])\n", + " movie1_id = int(item_vecs[i,0])\n", + " movie2_id = int(item_vecs[min_idx,0])\n", + " genre1,_ = get_item_genre(item_vecs[i,:], ivs, item_features)\n", + " genre2,_ = get_item_genre(item_vecs[min_idx,:], ivs, item_features)\n", + "\n", + " disp.append( [movie_dict[movie1_id]['title'], genre1,\n", + " movie_dict[movie2_id]['title'], genre2]\n", + " )\n", + "table = tabulate.tabulate(disp, tablefmt='html', headers=\"firstrow\", floatfmt=[\".1f\", \".1f\", \".0f\", \".2f\", \".2f\"])\n", + "table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results show the model will suggest a movie from the same genre." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4 - Congratulations! \n", + "You have completed a content-based recommender system. \n", + "\n", + "This structure is the basis of many commercial recommender systems. The user content can be greatly expanded to incorporate more information about the user if it is available. Items are not limited to movies. This can be used to recommend any item, books, cars or items that are similar to an item in your 'shopping cart'." + ] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyOFYdA6zQJ1FpgYwYmRIeXa", + "collapsed_sections": [], + "name": "Recsys_NN.ipynb", + "private_outputs": true, + "provenance": [ + { + "file_id": "1RO0HLb7kRE0Tj_0D4E5I-vQz2QLu3CUm", + "timestamp": 1655169179306 + } + ] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/data/content_item_train.csv b/Unsupervised learning recommenders 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+134393,2015,3.2,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134393,2015,3.2,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +134853,2015,3.813953488372093,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +135133,2015,3.7,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135133,2015,3.7,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +135536,2016,2.9166666666666665,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +135536,2016,2.9166666666666665,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +135536,2016,2.9166666666666665,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +135887,2015,3.1666666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +136020,2015,3.0588235294117645,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +136020,2015,3.0588235294117645,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +136020,2015,3.0588235294117645,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +168252,2017,4.28,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +168252,2017,4.28,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4306,2001,3.8676470588235294,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,0,1,0,0,0,0,0,0,0,0,0 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+116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +117529,2015,3.260869565217391,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +117529,2015,3.260869565217391,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +118696,2014,3.4166666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +118696,2014,3.4166666666666665,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +119145,2015,3.986111111111111,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122918,2017,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122922,2016,3.7045454545454546,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134130,2015,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +135143,2016,3.6785714285714284,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +139385,2015,3.903225806451613,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,0,0,0,0,0,0,1,0,0,0,0,0,0 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+115617,2014,3.8536585365853657,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +115713,2015,3.9107142857142856,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +115713,2015,3.9107142857142856,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +115713,2015,3.9107142857142856,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +116823,2014,3.869565217391304,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +117176,2014,3.7058823529411766,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +117176,2014,3.7058823529411766,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +119145,2015,3.986111111111111,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +129354,2015,3.25,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +129354,2015,3.25,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +129354,2015,3.25,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +129354,2015,3.25,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +134853,2015,3.813953488372093,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +135887,2015,3.1666666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +142488,2015,4.157894736842105,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +148626,2015,3.9615384615384617,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +166461,2016,3.45,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +168252,2017,4.28,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +168252,2017,4.28,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4148,2001,2.9358974358974357,0,0,0,0,0,0,0,0,0,1,0,0,0,0 +4148,2001,2.9358974358974357,0,0,0,0,0,0,0,0,0,0,0,0,0,1 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+96821,2012,3.825,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +97921,2012,3.716666666666667,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +97921,2012,3.716666666666667,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +105844,2013,3.625,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +106100,2013,3.9705882352941178,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +106782,2013,3.9166666666666665,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +106782,2013,3.9166666666666665,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +106782,2013,3.9166666666666665,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +106916,2013,3.25,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +106916,2013,3.25,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +106920,2013,3.92,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +106920,2013,3.92,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +106920,2013,3.92,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +109374,2014,3.7788461538461537,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +109374,2014,3.7788461538461537,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +112183,2014,3.3461538461538463,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +112183,2014,3.3461538461538463,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +112290,2014,3.823529411764706,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +112552,2014,4.0394736842105265,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +112556,2014,3.7162162162162162,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +112556,2014,3.7162162162162162,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +117176,2014,3.7058823529411766,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +117176,2014,3.7058823529411766,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +118696,2014,3.4166666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +118696,2014,3.4166666666666665,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +122892,2015,3.5185185185185186,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122892,2015,3.5185185185185186,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122892,2015,3.5185185185185186,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134853,2015,3.813953488372093,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +142488,2015,4.157894736842105,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +166461,2016,3.45,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +177765,2017,3.5384615384615383,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +177765,2017,3.5384615384615383,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +177765,2017,3.5384615384615383,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +179819,2017,3.125,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +179819,2017,3.125,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +179819,2017,3.125,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +179819,2017,3.125,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4896,2001,3.7616822429906542,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4896,2001,3.7616822429906542,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +4896,2001,3.7616822429906542,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +68954,2009,4.004761904761905,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +68954,2009,4.004761904761905,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +68954,2009,4.004761904761905,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +68954,2009,4.004761904761905,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +73321,2010,3.28125,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +73321,2010,3.28125,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +73321,2010,3.28125,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +96610,2012,3.640625,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +96610,2012,3.640625,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +96610,2012,3.640625,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +108932,2014,3.870967741935484,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +112852,2014,4.0508474576271185,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +112852,2014,4.0508474576271185,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +112852,2014,4.0508474576271185,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +115617,2014,3.8536585365853657,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +122892,2015,3.5185185185185186,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122892,2015,3.5185185185185186,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122892,2015,3.5185185185185186,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122900,2015,3.7222222222222223,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122900,2015,3.7222222222222223,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122900,2015,3.7222222222222223,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +152081,2016,3.890625,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4232,2001,2.9464285714285716,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +4232,2001,2.9464285714285716,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4232,2001,2.9464285714285716,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +4232,2001,2.9464285714285716,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4728,2001,3.0555555555555554,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4734,2001,3.161764705882353,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4734,2001,3.161764705882353,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4973,2001,4.183333333333334,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4973,2001,4.183333333333334,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +4979,2001,3.6838235294117645,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4979,2001,3.6838235294117645,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +5013,2001,3.7037037037037037,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +5013,2001,3.7037037037037037,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +5013,2001,3.7037037037037037,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +5377,2002,3.715909090909091,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +5377,2002,3.715909090909091,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +5377,2002,3.715909090909091,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +5388,2002,3.303030303030303,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +5388,2002,3.303030303030303,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +5388,2002,3.303030303030303,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +5388,2002,3.303030303030303,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +5388,2002,3.303030303030303,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +5505,2002,3.2083333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +5505,2002,3.2083333333333335,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +5577,2002,3.6666666666666665,0,0,0,0,1,0,0,0,0,0,0,0,0,0 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+122926,2017,4.15625,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122926,2017,4.15625,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +135536,2016,2.9166666666666665,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +135536,2016,2.9166666666666665,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +135536,2016,2.9166666666666665,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +136864,2016,2.34375,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +136864,2016,2.34375,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +136864,2016,2.34375,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +136864,2016,2.34375,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +142488,2015,4.157894736842105,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +143355,2017,3.3076923076923075,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +148626,2015,3.9615384615384617,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +168250,2017,3.6333333333333333,0,0,0,0,0,0,0,0,0,1,0,0,0,0 +176371,2017,3.8055555555555554,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4228,2001,2.15,0,0,0,0,1,0,0,0,0,0,0,0,0,0 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+134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +136864,2016,2.34375,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +136864,2016,2.34375,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +136864,2016,2.34375,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +136864,2016,2.34375,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +139385,2015,3.903225806451613,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +142488,2015,4.157894736842105,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +152081,2016,3.890625,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +4886,2001,3.871212121212121,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +5218,2002,3.6882352941176473,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +5218,2002,3.6882352941176473,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +5218,2002,3.6882352941176473,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +5218,2002,3.6882352941176473,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +6539,2003,3.778523489932886,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +6539,2003,3.778523489932886,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +6539,2003,3.778523489932886,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +6539,2003,3.778523489932886,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +8961,2004,3.836,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +45722,2006,3.5069444444444446,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +45722,2006,3.5069444444444446,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +45722,2006,3.5069444444444446,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +53125,2007,3.4375,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +53125,2007,3.4375,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +53125,2007,3.4375,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +53125,2007,3.4375,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +59315,2008,3.824468085106383,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +59315,2008,3.824468085106383,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +59315,2008,3.824468085106383,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +60069,2008,4.0576923076923075,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +60069,2008,4.0576923076923075,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +60069,2008,4.0576923076923075,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +60069,2008,4.0576923076923075,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +60069,2008,4.0576923076923075,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +84152,2011,3.95,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +84152,2011,3.95,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +91500,2012,3.435185185185185,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +91500,2012,3.435185185185185,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +91500,2012,3.435185185185185,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +91500,2012,3.435185185185185,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +91500,2012,3.435185185185185,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +102903,2013,3.409090909090909,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +102903,2013,3.409090909090909,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +102903,2013,3.409090909090909,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +115617,2014,3.8536585365853657,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +116823,2014,3.869565217391304,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122912,2018,4.0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122912,2018,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122912,2018,4.0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122916,2017,4.025,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122916,2017,4.025,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122916,2017,4.025,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122918,2017,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122922,2016,3.7045454545454546,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +135133,2015,3.7,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135133,2015,3.7,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +159093,2016,3.6363636363636362,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +159093,2016,3.6363636363636362,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +159093,2016,3.6363636363636362,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +166528,2016,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +176371,2017,3.8055555555555554,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +177765,2017,3.5384615384615383,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +177765,2017,3.5384615384615383,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +177765,2017,3.5384615384615383,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +187593,2018,3.875,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +187593,2018,3.875,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +187593,2018,3.875,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4069,2001,2.909090909090909,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4069,2001,2.909090909090909,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +4239,2001,3.8137254901960786,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +4239,2001,3.8137254901960786,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +4246,2001,3.623076923076923,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4246,2001,3.623076923076923,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +4246,2001,3.623076923076923,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +4247,2001,2.380952380952381,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4247,2001,2.380952380952381,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4247,2001,2.380952380952381,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +4247,2001,2.380952380952381,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +4254,2001,2.1923076923076925,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4254,2001,2.1923076923076925,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +4270,2001,3.088888888888889,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +4270,2001,3.088888888888889,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4270,2001,3.088888888888889,0,0,0,0,1,0,0,0,0,0,0,0,0,0 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+117529,2015,3.260869565217391,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +117590,2014,3.6363636363636362,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +117590,2014,3.6363636363636362,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +118696,2014,3.4166666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +118696,2014,3.4166666666666665,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +119141,2014,3.4583333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +119141,2014,3.4583333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +120466,2015,3.25,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +120466,2015,3.25,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122882,2015,3.8191489361702127,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122892,2015,3.5185185185185186,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122892,2015,3.5185185185185186,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122892,2015,3.5185185185185186,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122900,2015,3.7222222222222223,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122900,2015,3.7222222222222223,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122900,2015,3.7222222222222223,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122906,2017,3.727272727272727,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122906,2017,3.727272727272727,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122906,2017,3.727272727272727,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122912,2018,4.0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122912,2018,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122912,2018,4.0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122916,2017,4.025,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122916,2017,4.025,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122916,2017,4.025,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122918,2017,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122920,2016,3.6136363636363638,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122920,2016,3.6136363636363638,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122920,2016,3.6136363636363638,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122922,2016,3.7045454545454546,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122924,2016,3.0714285714285716,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122924,2016,3.0714285714285716,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122924,2016,3.0714285714285716,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +122924,2016,3.0714285714285716,0,0,0,0,0,0,0,0,0,0,0,0,1,0 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+135133,2015,3.7,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135133,2015,3.7,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +135569,2016,3.466666666666667,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +135569,2016,3.466666666666667,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135569,2016,3.466666666666667,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +135887,2015,3.1666666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +137857,2016,3.6363636363636362,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +137857,2016,3.6363636363636362,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +137857,2016,3.6363636363636362,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +138036,2015,3.4722222222222223,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +142488,2015,4.157894736842105,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +143355,2017,3.3076923076923075,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +148626,2015,3.9615384615384617,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +152077,2016,3.6785714285714284,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +152081,2016,3.890625,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +159093,2016,3.6363636363636362,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +159093,2016,3.6363636363636362,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +159093,2016,3.6363636363636362,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +166528,2016,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +166643,2016,3.8,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +168252,2017,4.28,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +168252,2017,4.28,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +176371,2017,3.8055555555555554,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +179819,2017,3.125,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +179819,2017,3.125,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +179819,2017,3.125,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +179819,2017,3.125,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +187593,2018,3.875,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +187593,2018,3.875,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +187593,2018,3.875,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4886,2001,3.871212121212121,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4886,2001,3.871212121212121,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +50872,2007,3.8680555555555554,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +50872,2007,3.8680555555555554,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +50872,2007,3.8680555555555554,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +6377,2003,3.9609929078014185,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +8961,2004,3.836,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +50872,2007,3.8680555555555554,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +50872,2007,3.8680555555555554,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +50872,2007,3.8680555555555554,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +66097,2009,3.7,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +66097,2009,3.7,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +66097,2009,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +81845,2010,4.043103448275862,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +90866,2011,3.75,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +90866,2011,3.75,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +90866,2011,3.75,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +95167,2012,3.466666666666667,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +95167,2012,3.466666666666667,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +95167,2012,3.466666666666667,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +95167,2012,3.466666666666667,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +97913,2012,3.75,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +97913,2012,3.75,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +108932,2014,3.870967741935484,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +115617,2014,3.8536585365853657,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122922,2016,3.7045454545454546,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134853,2015,3.813953488372093,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +137857,2016,3.6363636363636362,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +137857,2016,3.6363636363636362,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +137857,2016,3.6363636363636362,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +143355,2017,3.3076923076923075,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +152081,2016,3.890625,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +166461,2016,3.45,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +166528,2016,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +166643,2016,3.8,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +4973,2001,4.183333333333334,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4973,2001,4.183333333333334,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +5791,2002,3.78125,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +5791,2002,3.78125,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +5902,2002,3.9456521739130435,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +5902,2002,3.9456521739130435,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +5902,2002,3.9456521739130435,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +6016,2002,4.1466666666666665,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +6016,2002,4.1466666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +6016,2002,4.1466666666666665,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +6016,2002,4.1466666666666665,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +6016,2002,4.1466666666666665,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +6385,2002,3.75,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +6773,2003,3.7045454545454546,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +6773,2003,3.7045454545454546,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +6773,2003,3.7045454545454546,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +44555,2006,4.117647058823529,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +44555,2006,4.117647058823529,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +44555,2006,4.117647058823529,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +48394,2006,3.814814814814815,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +48394,2006,3.814814814814815,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +48394,2006,3.814814814814815,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +55442,2007,4.181818181818182,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +55442,2007,4.181818181818182,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +66097,2009,3.7,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +66097,2009,3.7,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +66097,2009,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +4306,2001,3.8676470588235294,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +4878,2001,3.981651376146789,0,0,0,0,0,0,0,1,0,0,0,0,0,0 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+117176,2014,3.7058823529411766,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +117176,2014,3.7058823529411766,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +117529,2015,3.260869565217391,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +117529,2015,3.260869565217391,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +117529,2015,3.260869565217391,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +118696,2014,3.4166666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +118696,2014,3.4166666666666665,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +122882,2015,3.8191489361702127,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +134130,2015,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134368,2015,3.6875,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +134368,2015,3.6875,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134368,2015,3.6875,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +135887,2015,3.1666666666666665,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +5445,2002,3.6375,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +5445,2002,3.6375,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +5445,2002,3.6375,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +5445,2002,3.6375,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +5445,2002,3.6375,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +5952,2002,4.0212765957446805,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +5952,2002,4.0212765957446805,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +4995,2001,4.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +4995,2001,4.0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +5989,2002,3.9217391304347826,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +5989,2002,3.9217391304347826,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +7147,2003,3.8333333333333335,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +7147,2003,3.8333333333333335,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +7147,2003,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +47099,2006,3.7934782608695654,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +48394,2006,3.814814814814815,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +48394,2006,3.814814814814815,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +48394,2006,3.814814814814815,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +48774,2006,3.9453125,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +48774,2006,3.9453125,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +48774,2006,3.9453125,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +48774,2006,3.9453125,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +48774,2006,3.9453125,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +60069,2008,4.0576923076923075,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +60069,2008,4.0576923076923075,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +60069,2008,4.0576923076923075,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +60069,2008,4.0576923076923075,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +60069,2008,4.0576923076923075,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +66097,2009,3.7,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +66097,2009,3.7,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +66097,2009,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +68954,2009,4.004761904761905,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +68954,2009,4.004761904761905,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +68954,2009,4.004761904761905,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +68954,2009,4.004761904761905,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +70286,2009,3.776923076923077,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +70286,2009,3.776923076923077,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +70286,2009,3.776923076923077,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +72641,2009,3.6774193548387095,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +115617,2014,3.8536585365853657,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +115617,2014,3.8536585365853657,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122916,2017,4.025,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122916,2017,4.025,0,1,0,0,0,0,0,0,0,0,0,0,0,0 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+139644,2015,3.5454545454545454,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +140110,2015,3.6538461538461537,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +142488,2015,4.157894736842105,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +148626,2015,3.9615384615384617,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +166528,2016,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +168252,2017,4.28,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +168252,2017,4.28,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +187593,2018,3.875,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +187593,2018,3.875,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +187593,2018,3.875,0,0,0,0,0,0,0,0,0,0,0,0,1,0 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+117590,2014,3.6363636363636362,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +117590,2014,3.6363636363636362,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +119141,2014,3.4583333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +119141,2014,3.4583333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122918,2017,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122918,2017,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134368,2015,3.6875,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +134368,2015,3.6875,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134368,2015,3.6875,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +140110,2015,3.6538461538461537,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +152077,2016,3.6785714285714284,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +152081,2016,3.890625,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +152081,2016,3.890625,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +157296,2016,3.576923076923077,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +166461,2016,3.45,0,0,0,0,0,0,0,0,1,0,0,0,0,0 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+103341,2013,3.4166666666666665,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +106918,2013,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +106918,2013,4.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +106918,2013,4.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +106920,2013,3.92,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +106920,2013,3.92,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +106920,2013,3.92,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +109374,2014,3.7788461538461537,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +109374,2014,3.7788461538461537,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +111360,2014,2.8214285714285716,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +111360,2014,2.8214285714285716,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +115569,2014,4.166666666666667,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +115569,2014,4.166666666666667,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +115569,2014,4.166666666666667,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +116823,2014,3.869565217391304,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +116823,2014,3.869565217391304,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +116897,2014,4.25,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +116897,2014,4.25,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +116897,2014,4.25,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +118696,2014,3.4166666666666665,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +118696,2014,3.4166666666666665,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +122882,2015,3.8191489361702127,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122906,2017,3.727272727272727,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122906,2017,3.727272727272727,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122906,2017,3.727272727272727,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134853,2015,3.813953488372093,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +135143,2016,3.6785714285714284,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +138036,2015,3.4722222222222223,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +138036,2015,3.4722222222222223,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +143355,2017,3.3076923076923075,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +148626,2015,3.9615384615384617,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +158238,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +166528,2016,3.925925925925926,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +166528,2016,3.925925925925926,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +176371,2017,3.8055555555555554,0,0,0,0,0,0,0,0,0,0,0,0,1,0 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+115569,2014,4.166666666666667,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122904,2016,3.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +122904,2016,3.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134130,2015,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134853,2015,3.813953488372093,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,1,0,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134853,2015,3.813953488372093,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +139385,2015,3.903225806451613,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +148626,2015,3.9615384615384617,0,0,0,0,0,0,0,1,0,0,0,0,0,0 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+44665,2006,3.8552631578947367,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +47099,2006,3.7934782608695654,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +48516,2006,4.252336448598131,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +48516,2006,4.252336448598131,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +48516,2006,4.252336448598131,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +59615,2008,2.8333333333333335,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +59615,2008,2.8333333333333335,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +59615,2008,2.8333333333333335,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +59615,2008,2.8333333333333335,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134130,2015,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +168252,2017,4.28,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +168252,2017,4.28,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4306,2001,3.8676470588235294,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,1,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,0,1,0,0,0,0,0,0,0,0,0,0 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+92259,2011,4.108108108108108,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +94677,2012,3.5588235294117645,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +96737,2012,3.7142857142857144,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +96737,2012,3.7142857142857144,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +105844,2013,3.625,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +107406,2013,3.4285714285714284,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +107406,2013,3.4285714285714284,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +107406,2013,3.4285714285714284,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +112623,2014,3.433333333333333,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +114935,2014,3.9,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +114935,2014,3.9,0,0,0,0,0,0,0,0,0,0,1,0,0,0 +114935,2014,3.9,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +114935,2014,3.9,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +115149,2014,3.8448275862068964,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +115149,2014,3.8448275862068964,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +115713,2015,3.9107142857142856,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +115713,2015,3.9107142857142856,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +115713,2015,3.9107142857142856,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +119145,2015,3.986111111111111,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +119145,2015,3.986111111111111,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +122882,2015,3.8191489361702127,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +122916,2017,4.025,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +122916,2017,4.025,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +122916,2017,4.025,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +134130,2015,4.0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +134130,2015,4.0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +139385,2015,3.903225806451613,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +139385,2015,3.903225806451613,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +164179,2016,3.980769230769231,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +4306,2001,3.8676470588235294,0,1,0,0,0,0,0,0,0,0,0,0,0,0 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+59369,2008,3.619047619047619,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +59369,2008,3.619047619047619,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +59369,2008,3.619047619047619,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +59369,2008,3.619047619047619,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +69122,2009,3.6315789473684212,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +69122,2009,3.6315789473684212,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +69406,2009,3.5,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +69406,2009,3.5,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +88163,2011,3.9838709677419355,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +88163,2011,3.9838709677419355,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +88163,2011,3.9838709677419355,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +104211,2013,3.738095238095238,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +104211,2013,3.738095238095238,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +134393,2015,3.2,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +134393,2015,3.2,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +140110,2015,3.6538461538461537,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +4306,2001,3.8676470588235294,0,0,1,0,0,0,0,0,0,0,0,0,0,0 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(2001),Drama|Romance +4069,"Wedding Planner, The (2001)",Comedy|Romance +4148,Hannibal (2001),Horror|Thriller +4149,Saving Silverman (Evil Woman) (2001),Comedy|Romance +4153,Down to Earth (2001),Comedy|Fantasy|Romance +4161,"Mexican, The (2001)",Action|Comedy +4167,15 Minutes (2001),Thriller +4228,Heartbreakers (2001),Comedy|Crime|Romance +4232,Spy Kids (2001),Action|Adventure|Children|Comedy +4238,Along Came a Spider (2001),Action|Crime|Mystery|Thriller +4239,Blow (2001),Crime|Drama +4246,Bridget Jones's Diary (2001),Comedy|Drama|Romance +4247,Joe Dirt (2001),Adventure|Comedy|Mystery|Romance +4254,Crocodile Dundee in Los Angeles (2001),Comedy|Drama +4270,"Mummy Returns, The (2001)",Action|Adventure|Comedy|Thriller +4299,"Knight's Tale, A (2001)",Action|Comedy|Romance +4306,Shrek (2001),Adventure|Animation|Children|Comedy|Fantasy|Romance +4340,"Animal, The (2001)",Comedy +4343,Evolution (2001),Comedy|Sci-Fi +4344,Swordfish (2001),Action|Crime|Drama +4366,Atlantis: The Lost Empire (2001),Adventure|Animation|Children|Fantasy +4367,Lara Croft: Tomb Raider (2001),Action|Adventure +4368,Dr. Dolittle 2 (2001),Comedy +4369,"Fast and the Furious, The (2001)",Action|Crime|Thriller +4370,A.I. Artificial Intelligence (2001),Adventure|Drama|Sci-Fi +4386,Cats & Dogs (2001),Children|Comedy +4388,Scary Movie 2 (2001),Comedy +4446,Final Fantasy: The Spirits Within (2001),Adventure|Animation|Fantasy|Sci-Fi +4447,Legally Blonde (2001),Comedy|Romance +4448,"Score, The (2001)",Action|Drama +4638,Jurassic Park III (2001),Action|Adventure|Sci-Fi|Thriller +4639,America's Sweethearts (2001),Comedy|Romance +4641,Ghost World (2001),Comedy|Drama +4643,Planet of the Apes (2001),Action|Adventure|Drama|Sci-Fi +4700,"Princess Diaries, The (2001)",Children|Comedy|Romance +4701,Rush Hour 2 (2001),Action|Comedy +4718,American Pie 2 (2001),Comedy +4720,"Others, The (2001)",Drama|Horror|Mystery|Thriller +4728,Rat Race (2001),Comedy +4734,Jay and Silent Bob Strike Back (2001),Adventure|Comedy +4776,Training Day (2001),Crime|Drama|Thriller +4816,Zoolander (2001),Comedy +4823,Serendipity (2001),Comedy|Romance +4865,From Hell (2001),Crime|Horror|Mystery|Thriller +4873,Waking Life (2001),Animation|Drama|Fantasy +4874,K-PAX (2001),Drama|Fantasy|Mystery|Sci-Fi +4876,Thirteen Ghosts (a.k.a. Thir13en Ghosts) (2001),Horror|Thriller +4878,Donnie Darko (2001),Drama|Mystery|Sci-Fi|Thriller +4881,"Man Who Wasn't There, The (2001)",Crime|Drama +4886,"Monsters, Inc. (2001)",Adventure|Animation|Children|Comedy|Fantasy +4887,"One, The (2001)",Action|Sci-Fi|Thriller +4890,Shallow Hal (2001),Comedy|Fantasy|Romance +4896,Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001),Adventure|Children|Fantasy +4901,Spy Game (2001),Action|Crime|Drama|Thriller +4903,In the Bedroom (2001),Drama +4963,Ocean's Eleven (2001),Crime|Thriller +4973,"Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)",Comedy|Romance +4974,Not Another Teen Movie (2001),Comedy +4975,Vanilla Sky (2001),Mystery|Romance|Sci-Fi|Thriller +4979,"Royal Tenenbaums, The (2001)",Comedy|Drama +4992,Kate & Leopold (2001),Comedy|Romance +4993,"Lord of the Rings: The Fellowship of the Ring, The (2001)",Adventure|Fantasy +4994,"Majestic, The (2001)",Comedy|Drama|Romance +4995,"Beautiful Mind, A (2001)",Drama|Romance +5013,Gosford Park (2001),Comedy|Drama|Mystery +5014,I Am Sam (2001),Drama +5015,Monster's Ball (2001),Drama|Romance +5025,Orange County (2002),Comedy +5026,"Brotherhood of the Wolf (Pacte des loups, Le) (2001)",Action|Mystery|Thriller +5064,The Count of Monte Cristo (2002),Action|Adventure|Drama|Thriller +5065,"Mothman Prophecies, The (2002)",Drama|Fantasy|Horror|Mystery|Thriller +5066,"Walk to Remember, A (2002)",Drama|Romance +5072,Metropolis (2001),Animation|Sci-Fi +5108,John Q (2002),Crime|Drama|Thriller +5110,Super Troopers (2001),Comedy|Crime|Mystery +5128,Queen of the Damned (2002),Fantasy|Horror +5135,Monsoon Wedding (2001),Comedy|Romance +5171,"Time Machine, The (2002)",Action|Adventure|Sci-Fi +5218,Ice Age (2002),Adventure|Animation|Children|Comedy +5219,Resident Evil (2002),Action|Horror|Sci-Fi|Thriller +5220,Showtime (2002),Action|Comedy +5254,Blade II (2002),Action|Horror|Thriller +5265,Death to Smoochy (2002),Comedy|Crime|Drama +5266,Panic Room (2002),Thriller +5267,"Rookie, The (2002)",Drama +5283,National Lampoon's Van Wilder (2002),Comedy +5294,Frailty (2001),Crime|Drama|Thriller +5296,"Sweetest Thing, The (2002)",Comedy|Romance +5299,My Big Fat Greek Wedding (2002),Comedy|Romance +5313,The Scorpion King (2002),Action|Adventure|Fantasy|Thriller +5329,"Salton Sea, The (2002)",Crime|Drama|Thriller +5349,Spider-Man (2002),Action|Adventure|Sci-Fi|Thriller +5377,About a Boy (2002),Comedy|Drama|Romance +5380,"Importance of Being Earnest, The (2002)",Comedy|Drama|Romance +5388,Insomnia (2002),Action|Crime|Drama|Mystery|Thriller +5400,"Sum of All Fears, The (2002)",Drama|Thriller +5418,"Bourne Identity, The (2002)",Action|Mystery|Thriller +5419,Scooby-Doo (2002),Adventure|Children|Comedy|Fantasy|Mystery +5444,Lilo & Stitch (2002),Adventure|Animation|Children|Sci-Fi +5445,Minority Report (2002),Action|Crime|Mystery|Sci-Fi|Thriller +5449,Mr. Deeds (2002),Comedy|Romance +5459,Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002),Action|Comedy|Sci-Fi +5463,Reign of Fire (2002),Action|Adventure|Fantasy +5464,Road to Perdition (2002),Crime|Drama +5481,Austin Powers in Goldmember (2002),Comedy +5502,Signs (2002),Horror|Sci-Fi|Thriller +5505,"Good Girl, The (2002)",Comedy|Drama +5507,xXx (2002),Action|Crime|Thriller +5528,One Hour Photo (2002),Drama|Thriller +5574,"Transporter, The (2002)",Action|Crime +5577,Igby Goes Down (2002),Comedy|Drama +5608,"Das Experiment (Experiment, The) (2001)",Drama|Thriller +5618,Spirited Away (Sen to Chihiro no kamikakushi) (2001),Adventure|Animation|Fantasy +5620,Sweet Home Alabama (2002),Comedy|Romance +5621,"Tuxedo, The (2002)",Action|Comedy +5630,Red Dragon (2002),Crime|Mystery|Thriller +5669,Bowling for Columbine (2002),Documentary +5673,Punch-Drunk Love (2002),Comedy|Drama|Romance +5679,"Ring, The (2002)",Horror|Mystery|Thriller +5785,Jackass: The Movie (2002),Action|Comedy|Documentary +5791,Frida (2002),Drama|Romance +5810,8 Mile (2002),Drama +5812,Far from Heaven (2002),Drama|Romance +5816,Harry Potter and the Chamber of Secrets (2002),Adventure|Fantasy +5872,Die Another Day (2002),Action|Adventure|Thriller +5878,Talk to Her (Hable con Ella) (2002),Drama|Romance +5881,Solaris (2002),Drama|Romance|Sci-Fi +5902,Adaptation (2002),Comedy|Drama|Romance +5903,Equilibrium (2002),Action|Sci-Fi|Thriller +5943,Maid in Manhattan (2002),Comedy|Romance +5944,Star Trek: Nemesis (2002),Action|Drama|Sci-Fi|Thriller +5945,About Schmidt (2002),Comedy|Drama +5952,"Lord of the Rings: The Two Towers, The (2002)",Adventure|Fantasy +5954,25th Hour (2002),Crime|Drama +5956,Gangs of New York (2002),Crime|Drama +5957,Two Weeks Notice (2002),Comedy|Romance +5989,Catch Me If You Can (2002),Crime|Drama +5992,"Hours, The (2002)",Drama|Romance +6003,Confessions of a Dangerous Mind (2002),Comedy|Crime|Drama|Thriller +6016,City of God (Cidade de Deus) (2002),Action|Adventure|Crime|Drama|Thriller +6058,Final Destination 2 (2003),Horror|Thriller +6059,"Recruit, The (2003)",Action|Thriller +6155,How to Lose a Guy in 10 Days (2003),Comedy|Romance +6156,Shanghai Knights (2003),Action|Adventure|Comedy +6157,Daredevil (2003),Action|Crime +6188,Old School (2003),Comedy +6218,Bend It Like Beckham (2002),Comedy|Drama|Romance +6281,Phone Booth (2002),Drama|Thriller +6283,Cowboy Bebop: The Movie (Cowboy Bebop: Tengoku no Tobira) (2001),Action|Animation|Sci-Fi|Thriller +6287,Anger Management (2003),Comedy +6294,Bulletproof Monk (2003),Action|Adventure|Sci-Fi +6297,Holes (2003),Adventure|Children|Comedy|Mystery +6323,Identity (2003),Crime|Horror|Mystery|Thriller +6331,Spellbound (2002),Documentary +6333,X2: X-Men United (2003),Action|Adventure|Sci-Fi|Thriller +6367,Down with Love (2003),Comedy|Romance +6373,Bruce Almighty (2003),Comedy|Drama|Fantasy|Romance +6377,Finding Nemo (2003),Adventure|Animation|Children|Comedy +6378,"Italian Job, The (2003)",Action|Crime +6383,"2 Fast 2 Furious (Fast and the Furious 2, The) (2003)",Action|Crime|Thriller +6385,Whale Rider (2002),Drama +6482,Dumb and Dumberer: When Harry Met Lloyd (2003),Comedy +6502,28 Days Later (2002),Action|Horror|Sci-Fi +6503,Charlie's Angels: Full Throttle (2003),Action|Adventure|Comedy|Crime|Thriller +6534,Hulk (2003),Action|Adventure|Sci-Fi +6535,"Legally Blonde 2: Red, White & Blonde (2003)",Comedy +6537,Terminator 3: Rise of the Machines (2003),Action|Adventure|Sci-Fi +6539,Pirates of the Caribbean: The Curse of the Black Pearl (2003),Action|Adventure|Comedy|Fantasy +6541,"League of Extraordinary Gentlemen, The (a.k.a. LXG) (2003)",Action|Fantasy|Sci-Fi +6548,Bad Boys II (2003),Action|Comedy|Crime|Thriller +6550,Johnny English (2003),Action|Comedy|Thriller +6552,Dirty Pretty Things (2002),Crime|Drama|Thriller +6564,Lara Croft Tomb Raider: The Cradle of Life (2003),Action|Adventure|Comedy|Romance|Thriller +6565,Seabiscuit (2003),Drama +6586,American Wedding (American Pie 3) (2003),Comedy +6593,Freaky Friday (2003),Children|Comedy|Fantasy +6595,S.W.A.T. (2003),Action|Thriller +6618,Shaolin Soccer (Siu lam juk kau) (2001),Action|Comedy +6620,American Splendor (2003),Comedy|Drama +6708,Matchstick Men (2003),Comedy|Crime|Drama +6709,Once Upon a Time in Mexico (2003),Action|Adventure|Crime|Thriller +6711,Lost in Translation (2003),Comedy|Drama|Romance +6753,Secondhand Lions (2003),Children|Comedy|Drama +6754,Underworld (2003),Action|Fantasy|Horror +6755,Bubba Ho-tep (2002),Comedy|Horror +6764,"Rundown, The (2003)",Action|Adventure|Comedy +6773,"Triplets of Belleville, The (Les triplettes de Belleville) (2003)",Animation|Comedy|Fantasy +6867,"Station Agent, The (2003)",Comedy|Drama +6870,Mystic River (2003),Crime|Drama|Mystery +6873,Intolerable Cruelty (2003),Comedy|Romance +6874,Kill Bill: Vol. 1 (2003),Action|Crime|Thriller +6879,Runaway Jury (2003),Drama|Thriller +6888,Scary Movie 3 (2003),Comedy|Horror +6936,Elf (2003),Children|Comedy|Fantasy +6942,Love Actually (2003),Comedy|Drama|Romance +6953,21 Grams (2003),Crime|Drama|Mystery|Romance|Thriller +6957,Bad Santa (2003),Comedy|Crime +6958,"Haunted Mansion, The (2003)",Children|Comedy|Fantasy|Horror +7090,Hero (Ying xiong) (2002),Action|Adventure|Drama +7137,"Cooler, The (2003)",Comedy|Drama|Romance +7147,Big Fish (2003),Drama|Fantasy|Romance +7149,Something's Gotta Give (2003),Comedy|Drama|Romance +7150,Stuck on You (2003),Comedy +7151,Girl with a Pearl Earring (2003),Drama|Romance +7153,"Lord of the Rings: The Return of the King, The (2003)",Action|Adventure|Drama|Fantasy +7154,Mona Lisa Smile (2003),Drama|Romance +7160,Monster (2003),Crime|Drama +7163,Paycheck (2003),Action|Sci-Fi|Thriller +7173,Along Came Polly (2004),Comedy|Romance +7254,The Butterfly Effect (2004),Drama|Sci-Fi|Thriller +7263,Miracle (2004),Drama +7265,"Dreamers, The (2003)",Drama +7293,50 First Dates (2004),Comedy|Romance +7317,EuroTrip (2004),Adventure|Comedy +7323,"Good bye, Lenin! (2003)",Comedy|Drama +7324,Hidalgo (2004),Adventure|Drama +7325,Starsky & Hutch (2004),Action|Comedy|Crime|Thriller +7347,Secret Window (2004),Mystery|Thriller +7360,Dawn of the Dead (2004),Action|Drama|Horror|Thriller +7361,Eternal Sunshine of the Spotless Mind (2004),Drama|Romance|Sci-Fi +7367,"Ladykillers, The (2004)",Comedy|Crime +7371,Dogville (2003),Drama|Mystery|Thriller +7373,Hellboy (2004),Action|Adventure|Fantasy|Horror +7438,Kill Bill: Vol. 2 (2004),Action|Drama|Thriller +7439,"Punisher, The (2004)",Action|Crime|Thriller +7444,13 Going on 30 (2004),Comedy|Fantasy|Romance +7445,Man on Fire (2004),Action|Crime|Drama|Mystery|Thriller +7451,Mean Girls (2004),Comedy +7454,Van Helsing (2004),Action|Adventure|Fantasy|Horror +8014,"Spring, Summer, Fall, Winter... and Spring (Bom yeoreum gaeul gyeoul geurigo bom) (2003)",Drama +8361,"Day After Tomorrow, The (2004)",Action|Adventure|Drama|Sci-Fi|Thriller +8366,Saved! (2004),Comedy|Drama +8370,"Blind Swordsman: Zatoichi, The (Zatôichi) (2003)",Action|Comedy|Crime|Drama +8371,"Chronicles of Riddick, The (2004)",Action|Sci-Fi|Thriller +8373,"Stepford Wives, The (2004)",Comedy|Fantasy|Thriller +8376,Napoleon Dynamite (2004),Comedy +8464,Super Size Me (2004),Comedy|Documentary|Drama +8528,Dodgeball: A True Underdog Story (2004),Comedy +8529,"Terminal, The (2004)",Comedy|Drama|Romance +8533,"Notebook, The (2004)",Drama|Romance +8622,Fahrenheit 9/11 (2004),Documentary +8638,Before Sunset (2004),Drama|Romance +8641,Anchorman: The Legend of Ron Burgundy (2004),Comedy +8644,"I, Robot (2004)",Action|Adventure|Sci-Fi|Thriller +8665,"Bourne Supremacy, The (2004)",Action|Crime|Thriller +8781,"Manchurian Candidate, The (2004)",Thriller +8783,"Village, The (2004)",Drama|Mystery|Thriller +8784,Garden State (2004),Comedy|Drama|Romance +8798,Collateral (2004),Action|Crime|Drama|Thriller +8807,Harold and Kumar Go to White Castle (2004),Adventure|Comedy +8810,AVP: Alien vs. Predator (2004),Action|Horror|Sci-Fi|Thriller +8861,Resident Evil: Apocalypse (2004),Action|Horror|Sci-Fi|Thriller +8865,Sky Captain and the World of Tomorrow (2004),Action|Adventure|Sci-Fi +8873,"Motorcycle Diaries, The (Diarios de motocicleta) (2004)",Adventure|Drama +8874,Shaun of the Dead (2004),Comedy|Horror +8907,Shark Tale (2004),Animation|Children|Comedy +8910,I Heart Huckabees (2004),Comedy +8914,Primer (2004),Drama|Sci-Fi +8917,Team America: World Police (2004),Action|Adventure|Animation|Comedy +8947,"Grudge, The (2004)",Horror|Mystery|Thriller +8949,Sideways (2004),Comedy|Drama|Romance +8950,The Machinist (2004),Drama|Mystery|Thriller +8957,Saw (2004),Horror|Mystery|Thriller +8958,Ray (2004),Drama +8961,"Incredibles, The (2004)",Action|Adventure|Animation|Children|Comedy +8969,Bridget Jones: The Edge of Reason (2004),Comedy|Drama|Romance +8970,Finding Neverland (2004),Drama +8972,National Treasure (2004),Action|Adventure|Drama|Mystery|Thriller +8981,Closer (2004),Drama|Romance +8983,House of Flying Daggers (Shi mian mai fu) (2004),Action|Drama|Romance +8984,Ocean's Twelve (2004),Action|Comedy|Crime|Thriller +8985,Blade: Trinity (2004),Action|Fantasy|Horror|Thriller +27660,"Animatrix, The (2003)",Action|Animation|Drama|Sci-Fi +27706,Lemony Snicket's A Series of Unfortunate Events (2004),Adventure|Children|Comedy|Fantasy +27773,Old Boy (2003),Mystery|Thriller +27801,Ong-Bak: The Thai Warrior (Ong Bak) (2003),Action|Thriller +27808,Spanglish (2004),Comedy|Drama|Romance +27815,"Chorus, The (Choristes, Les) (2004)",Drama +27831,Layer Cake (2004),Crime|Drama|Thriller +27846,"Corporation, The (2003)",Documentary +27904,"Scanner Darkly, A (2006)",Animation|Drama|Mystery|Sci-Fi|Thriller +30707,Million Dollar Baby (2004),Drama +30810,"Life Aquatic with Steve Zissou, The (2004)",Adventure|Comedy|Fantasy +30812,"Aviator, The (2004)",Drama +30822,In Good Company (2004),Comedy|Drama +30825,Meet the Fockers (2004),Comedy +31221,Elektra (2005),Action|Adventure|Crime|Drama +31433,"Wedding Date, The (2005)",Comedy|Romance +31658,Howl's Moving Castle (Hauru no ugoku shiro) (2004),Adventure|Animation|Fantasy|Romance +31685,Hitch (2005),Comedy|Romance +31696,Constantine (2005),Action|Fantasy|Horror|Thriller +31878,Kung Fu Hustle (Gong fu) (2004),Action|Comedy +32029,Hostage (2005),Action|Crime|Drama|Thriller +33004,"Hitchhiker's Guide to the Galaxy, The (2005)",Adventure|Comedy|Sci-Fi +33166,Crash (2004),Crime|Drama +33493,Star Wars: Episode III - Revenge of the Sith (2005),Action|Adventure|Sci-Fi +33615,Madagascar (2005),Adventure|Animation|Children|Comedy +33660,Cinderella Man (2005),Drama|Romance +33679,Mr. & Mrs. Smith (2005),Action|Adventure|Comedy|Romance +33836,Bewitched (2005),Comedy|Fantasy|Romance +34048,War of the Worlds (2005),Action|Adventure|Sci-Fi|Thriller +34072,"March of the Penguins (Marche de l'empereur, La) (2005)",Documentary +34150,Fantastic Four (2005),Action|Adventure|Sci-Fi +34162,Wedding Crashers (2005),Comedy|Romance +34319,"Island, The (2005)",Action|Sci-Fi|Thriller +34405,Serenity (2005),Action|Adventure|Sci-Fi +34437,Broken Flowers (2005),Comedy|Drama +34520,"Dukes of Hazzard, The (2005)",Action|Adventure|Comedy +35836,"40-Year-Old Virgin, The (2005)",Comedy|Romance +36401,"Brothers Grimm, The (2005)",Comedy|Fantasy|Horror|Thriller +36517,"Constant Gardener, The (2005)",Drama|Thriller +36519,Transporter 2 (2005),Action|Crime|Thriller +36708,Family Guy Presents Stewie Griffin: The Untold Story (2005),Adventure|Animation|Comedy +37380,Doom (2005),Action|Horror|Sci-Fi +37384,Waiting... (2005),Comedy +37386,Aeon Flux (2005),Action|Sci-Fi +37727,Flightplan (2005),Action|Drama|Thriller +37733,"History of Violence, A (2005)",Action|Crime|Drama|Thriller +37741,Capote (2005),Crime|Drama +37830,Final Fantasy VII: Advent Children (2004),Action|Adventure|Animation|Fantasy|Sci-Fi +38038,Wallace & Gromit in The Curse of the Were-Rabbit (2005),Adventure|Animation|Children|Comedy +38061,Kiss Kiss Bang Bang (2005),Comedy|Crime|Mystery|Thriller +39183,Brokeback Mountain (2005),Drama|Romance +39292,"Good Night, and Good Luck. (2005)",Crime|Drama +39446,Saw II (2005),Horror|Thriller +40583,Syriana (2005),Drama|Thriller +40629,Pride & Prejudice (2005),Drama|Romance +40732,"Descent, The (2005)",Adventure|Drama|Horror|Thriller +41285,Match Point (2005),Crime|Drama|Romance +41566,"Chronicles of Narnia: The Lion, the Witch and the Wardrobe, The (2005)",Adventure|Children|Fantasy +41569,King Kong (2005),Action|Adventure|Drama|Fantasy|Thriller +41997,Munich (2005),Action|Crime|Drama|Thriller +42011,Fun with Dick and Jane (2005),Comedy|Crime +42723,Hostel (2005),Horror +42738,Underworld: Evolution (2006),Action|Fantasy|Horror +43928,Ultraviolet (2006),Action|Fantasy|Sci-Fi|Thriller +44022,Ice Age 2: The Meltdown (2006),Adventure|Animation|Children|Comedy +44195,Thank You for Smoking (2006),Comedy|Drama +44199,Inside Man (2006),Crime|Drama|Thriller +44555,"Lives of Others, The (Das leben der Anderen) (2006)",Drama|Romance|Thriller +44665,Lucky Number Slevin (2006),Crime|Drama|Mystery +44840,"Benchwarmers, The (2006)",Comedy +44972,Scary Movie 4 (2006),Comedy|Horror +44974,Hard Candy (2005),Drama|Thriller +45081,Silent Hill (2006),Fantasy|Horror|Thriller +45186,Mission: Impossible III (2006),Action|Adventure|Thriller +45431,Over the Hedge (2006),Adventure|Animation|Children|Comedy +45447,"Da Vinci Code, The (2006)",Drama|Mystery|Thriller +45499,X-Men: The Last Stand (2006),Action|Sci-Fi|Thriller +45501,"Break-Up, The (2006)",Comedy|Drama|Romance +45517,Cars (2006),Animation|Children|Comedy +45666,Nacho Libre (2006),Comedy +45668,"Lake House, The (2006)",Drama|Fantasy|Romance +45672,Click (2006),Adventure|Comedy|Drama|Fantasy|Romance +45720,"Devil Wears Prada, The (2006)",Comedy|Drama +45722,Pirates of the Caribbean: Dead Man's Chest (2006),Action|Adventure|Fantasy +45728,Clerks II (2006),Comedy +45880,Marie Antoinette (2006),Drama|Romance +45950,"Inconvenient Truth, An (2006)",Documentary +46335,"Fast and the Furious: Tokyo Drift, The (Fast and the Furious 3, The) (2006)",Action|Crime|Drama|Thriller +46578,Little Miss Sunshine (2006),Adventure|Comedy|Drama +46723,Babel (2006),Drama|Thriller +46965,Snakes on a Plane (2006),Action|Comedy|Horror|Thriller +46970,Talladega Nights: The Ballad of Ricky Bobby (2006),Action|Comedy +46976,Stranger than Fiction (2006),Comedy|Drama|Fantasy|Romance +47044,Miami Vice (2006),Action|Crime|Drama|Thriller +47099,"Pursuit of Happyness, The (2006)",Drama +47200,Crank (2006),Action|Thriller +47518,Accepted (2006),Comedy +47610,"Illusionist, The (2006)",Drama|Fantasy|Mystery|Romance +47629,The Queen (2006),Drama +47997,Idiocracy (2006),Adventure|Comedy|Sci-Fi|Thriller +48043,"Fountain, The (2006)",Drama|Fantasy|Romance +48082,"Science of Sleep, The (La science des rêves) (2006)",Comedy|Drama|Fantasy|Romance +48304,Apocalypto (2006),Adventure|Drama|Thriller +48385,Borat: Cultural Learnings of America for Make Benefit Glorious Nation of Kazakhstan (2006),Comedy +48394,"Pan's Labyrinth (Laberinto del fauno, El) (2006)",Drama|Fantasy|Thriller +48516,"Departed, The (2006)",Crime|Drama|Thriller +48738,"Last King of Scotland, The (2006)",Drama|Thriller +48774,Children of Men (2006),Action|Adventure|Drama|Sci-Fi|Thriller +48780,"Prestige, The (2006)",Drama|Mystery|Sci-Fi|Thriller +48877,Saw III (2006),Crime|Horror|Thriller +49272,Casino Royale (2006),Action|Adventure|Thriller +49278,Déjà Vu (Deja Vu) (2006),Action|Sci-Fi|Thriller +49286,"Holiday, The (2006)",Comedy|Romance +49649,Eragon (2006),Action|Adventure|Fantasy +49651,Rocky Balboa (2006),Action|Drama +50794,Smokin' Aces (2006),Action|Crime|Drama|Thriller +50872,Ratatouille (2007),Animation|Children|Drama +51077,Ghost Rider (2007),Action|Fantasy|Thriller +51084,Music and Lyrics (2007),Comedy|Romance +51086,"Number 23, The (2007)",Drama|Mystery|Thriller +51255,Hot Fuzz (2007),Action|Comedy|Crime|Mystery +51412,Next (2007),Action|Sci-Fi|Thriller +51540,Zodiac (2007),Crime|Drama|Thriller +51935,Shooter (2007),Action|Drama|Thriller +52245,Blades of Glory (2007),Comedy|Romance +52281,Grindhouse (2007),Action|Crime|Horror|Sci-Fi|Thriller +52287,Meet the Robinsons (2007),Action|Adventure|Animation|Children|Comedy|Sci-Fi +52328,Sunshine (2007),Adventure|Drama|Sci-Fi|Thriller +52458,Disturbia (2007),Drama|Thriller +52604,Fracture (2007),Crime|Drama|Mystery|Thriller +52973,Knocked Up (2007),Comedy|Drama|Romance +53000,28 Weeks Later (2007),Horror|Sci-Fi|Thriller +53121,Shrek the Third (2007),Adventure|Animation|Children|Comedy|Fantasy +53125,Pirates of the Caribbean: At World's End (2007),Action|Adventure|Comedy|Fantasy +53322,Ocean's Thirteen (2007),Crime|Thriller +53464,Fantastic Four: Rise of the Silver Surfer (2007),Action|Adventure|Sci-Fi +53519,Death Proof (2007),Action|Adventure|Crime|Horror|Thriller +53894,Sicko (2007),Documentary|Drama +53953,1408 (2007),Drama|Horror|Thriller +53972,Live Free or Die Hard (2007),Action|Adventure|Crime|Thriller +53993,Evan Almighty (2007),Comedy|Fantasy +54004,I Now Pronounce You Chuck and Larry (2007),Comedy|Romance +54259,Stardust (2007),Adventure|Comedy|Fantasy|Romance +54272,"Simpsons Movie, The (2007)",Animation|Comedy +54286,"Bourne Ultimatum, The (2007)",Action|Crime|Thriller +54503,Superbad (2007),Comedy +54881,"King of Kong, The (2007)",Documentary +54995,Planet Terror (2007),Action|Horror|Sci-Fi +54999,Shoot 'Em Up (2007),Action|Comedy|Crime +55118,Eastern Promises (2007),Crime|Drama|Thriller +55232,Resident Evil: Extinction (2007),Action|Horror|Sci-Fi|Thriller +55247,Into the Wild (2007),Action|Adventure|Drama +55269,"Darjeeling Limited, The (2007)",Adventure|Comedy|Drama +55276,Michael Clayton (2007),Drama|Thriller +55282,30 Days of Night (2007),Horror|Thriller +55290,Gone Baby Gone (2007),Crime|Drama|Mystery +55442,Persepolis (2007),Animation|Drama +55721,Elite Squad (Tropa de Elite) (2007),Action|Crime|Drama|Thriller +55765,American Gangster (2007),Crime|Drama|Thriller +55768,Bee Movie (2007),Animation|Comedy +55820,No Country for Old Men (2007),Crime|Drama +55830,Be Kind Rewind (2008),Comedy +56145,"Mist, The (2007)",Horror|Sci-Fi +56171,"Golden Compass, The (2007)",Adventure|Children|Fantasy +56251,Futurama: Bender's Big Score (2007),Animation|Comedy|Sci-Fi +56367,Juno (2007),Comedy|Drama|Romance +56587,"Bucket List, The (2007)",Comedy|Drama +56775,National Treasure: Book of Secrets (2007),Action|Adventure +56941,P.S. I Love You (2007),Comedy|Drama|Romance +56949,27 Dresses (2008),Comedy|Romance +57368,Cloverfield (2008),Action|Mystery|Sci-Fi|Thriller +57504,"Girl Who Leapt Through Time, The (Toki o kakeru shôjo) (2006)",Animation|Comedy|Drama|Romance|Sci-Fi +57640,Hellboy II: The Golden Army (2008),Action|Adventure|Fantasy|Sci-Fi +57669,In Bruges (2008),Comedy|Crime|Drama|Thriller +58025,Jumper (2008),Action|Adventure|Drama|Sci-Fi|Thriller +58047,"Definitely, Maybe (2008)",Comedy|Drama|Romance +58156,Semi-Pro (2008),Comedy +58293,"10,000 BC (2008)",Adventure|Romance|Thriller +58295,"Bank Job, The (2008)",Action|Crime|Thriller +58803,21 (2008),Crime|Drama|Romance|Thriller +58998,Forgetting Sarah Marshall (2008),Comedy|Romance +59022,Harold & Kumar Escape from Guantanamo Bay (2008),Adventure|Comedy +59258,Baby Mama (2008),Comedy +59315,Iron Man (2008),Action|Adventure|Sci-Fi +59369,Taken (2008),Action|Crime|Drama|Thriller +59387,"Fall, The (2006)",Adventure|Drama|Fantasy +59501,"Chronicles of Narnia: Prince Caspian, The (2008)",Adventure|Children|Fantasy +59615,Indiana Jones and the Kingdom of the Crystal Skull (2008),Action|Adventure|Comedy|Sci-Fi +59725,Sex and the City (2008),Comedy|Romance +59900,You Don't Mess with the Zohan (2008),Comedy +60040,"Incredible Hulk, The (2008)",Action|Sci-Fi +60069,WALL·E (2008),Adventure|Animation|Children|Romance|Sci-Fi +60072,Wanted (2008),Action|Thriller +60074,Hancock (2008),Action|Adventure|Comedy|Crime|Fantasy +60126,Get Smart (2008),Action|Comedy +60756,Step Brothers (2008),Comedy +60950,Vicky Cristina Barcelona (2008),Comedy|Drama|Romance +61024,Pineapple Express (2008),Action|Comedy|Crime +61240,Let the Right One In (Låt den rätte komma in) (2008),Drama|Fantasy|Horror|Romance +61323,Burn After Reading (2008),Comedy|Crime|Drama +62155,Nick and Norah's Infinite Playlist (2008),Comedy|Drama|Romance +62374,Body of Lies (2008),Action|Drama|Thriller +62849,RocknRolla (2008),Action|Crime +63082,Slumdog Millionaire (2008),Crime|Drama|Romance 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(2009)",Action|Crime|Drama|Thriller +68135,17 Again (2009),Comedy|Drama +68237,Moon (2009),Drama|Mystery|Sci-Fi|Thriller +68319,X-Men Origins: Wolverine (2009),Action|Sci-Fi|Thriller +68554,Angels & Demons (2009),Crime|Drama|Mystery|Thriller +68791,Terminator Salvation (2009),Action|Adventure|Sci-Fi|Thriller +68954,Up (2009),Adventure|Animation|Children|Drama +69122,"Hangover, The (2009)",Comedy|Crime +69406,"Proposal, The (2009)",Comedy|Romance +69644,Ice Age: Dawn of the Dinosaurs (2009),Action|Adventure|Animation|Children|Comedy|Romance +69757,(500) Days of Summer (2009),Comedy|Drama|Romance +69784,Brüno (Bruno) (2009),Comedy +69951,"Imaginarium of Doctor Parnassus, The (2009)",Drama|Fantasy +70183,"Ugly Truth, The (2009)",Comedy|Drama|Romance +70286,District 9 (2009),Mystery|Sci-Fi|Thriller +70293,Julie & Julia (2009),Comedy|Drama|Romance +71057,9 (2009),Adventure|Animation|Sci-Fi +71464,"Serious Man, A (2009)",Comedy|Drama +71530,Surrogates (2009),Action|Sci-Fi|Thriller 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(2010)",Action|Adventure|Children|Comedy|Fantasy +79293,Salt (2010),Action|Thriller +79592,"Other Guys, The (2010)",Action|Comedy +79695,"Expendables, The (2010)",Action|Adventure|Thriller +80219,Machete (2010),Action|Adventure|Comedy|Crime|Thriller +80463,"Social Network, The (2010)",Drama +80489,"Town, The (2010)",Crime|Drama|Thriller +80549,Easy A (2010),Comedy|Romance +80906,Inside Job (2010),Documentary +81229,Red (2010),Action|Comedy +81537,Due Date (2010),Comedy +81562,127 Hours (2010),Adventure|Drama|Thriller +81591,Black Swan (2010),Drama|Thriller +81845,"King's Speech, The (2010)",Drama +81932,"Fighter, The (2010)",Drama +83134,Tucker & Dale vs Evil (2010),Comedy|Horror +84152,Limitless (2011),Sci-Fi|Thriller +84374,No Strings Attached (2011),Comedy|Romance +84392,"Lincoln Lawyer, The (2011)",Crime|Drama|Thriller +84772,Paul (2011),Adventure|Comedy|Sci-Fi +84954,"Adjustment Bureau, The (2011)",Romance|Sci-Fi|Thriller +85414,Source Code 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Rogue Nation (2015),Action|Adventure|Thriller +112138,22 Jump Street (2014),Action|Comedy|Crime +112175,How to Train Your Dragon 2 (2014),Action|Adventure|Animation +112183,Birdman: Or (The Unexpected Virtue of Ignorance) (2014),Comedy|Drama +112290,Boyhood (2014),Drama +112552,Whiplash (2014),Drama +112556,Gone Girl (2014),Drama|Thriller +112623,Dawn of the Planet of the Apes (2014),Sci-Fi +112852,Guardians of the Galaxy (2014),Action|Adventure|Sci-Fi +114180,"Maze Runner, The (2014)",Action|Mystery|Sci-Fi +114935,Predestination (2014),Action|Mystery|Sci-Fi|Thriller +115149,John Wick (2014),Action|Thriller +115569,Nightcrawler (2014),Crime|Drama|Thriller +115617,Big Hero 6 (2014),Action|Animation|Comedy +115713,Ex Machina (2015),Drama|Sci-Fi|Thriller +116823,The Hunger Games: Mockingjay - Part 1 (2014),Adventure|Sci-Fi|Thriller +116897,Wild Tales (2014),Comedy|Drama|Thriller +117176,The Theory of Everything (2014),Drama|Romance +117529,Jurassic World (2015),Action|Adventure|Drama|Sci-Fi|Thriller +117590,Horrible Bosses 2 (2014),Comedy|Crime +118696,The Hobbit: The Battle of the Five Armies (2014),Adventure|Fantasy +119141,The Interview (2014),Action|Comedy +119145,Kingsman: The Secret Service (2015),Action|Adventure|Comedy|Crime +120466,Chappie (2015),Action|Thriller +122882,Mad Max: Fury Road (2015),Action|Adventure|Sci-Fi|Thriller +122892,Avengers: Age of Ultron (2015),Action|Adventure|Sci-Fi +122900,Ant-Man (2015),Action|Adventure|Sci-Fi +122904,Deadpool (2016),Action|Adventure|Comedy|Sci-Fi +122906,Black Panther (2017),Action|Adventure|Sci-Fi +122912,Avengers: Infinity War - Part I (2018),Action|Adventure|Sci-Fi +122916,Thor: Ragnarok (2017),Action|Adventure|Sci-Fi +122918,Guardians of the Galaxy 2 (2017),Action|Adventure|Sci-Fi +122920,Captain America: Civil War (2016),Action|Sci-Fi|Thriller +122922,Doctor Strange (2016),Action|Adventure|Sci-Fi +122924,X-Men: Apocalypse (2016),Action|Adventure|Fantasy|Sci-Fi +122926,Untitled Spider-Man Reboot (2017),Action|Adventure|Fantasy +129354,Focus (2015),Comedy|Crime|Drama|Romance +134130,The Martian (2015),Adventure|Drama|Sci-Fi +134368,Spy (2015),Action|Comedy|Crime +134393,Trainwreck (2015),Comedy|Romance +134853,Inside Out (2015),Adventure|Animation|Children|Comedy|Drama|Fantasy +135133,The Hunger Games: Mockingjay - Part 2 (2015),Adventure|Sci-Fi +135143,Fantastic Beasts and Where to Find Them (2016),Fantasy +135536,Suicide Squad (2016),Action|Crime|Sci-Fi +135569,Star Trek Beyond (2016),Action|Adventure|Sci-Fi +135887,Minions (2015),Adventure|Animation|Children|Comedy +136020,Spectre (2015),Action|Adventure|Crime +136864,Batman v Superman: Dawn of Justice (2016),Action|Adventure|Fantasy|Sci-Fi +137857,The Jungle Book (2016),Adventure|Drama|Fantasy +138036,The Man from U.N.C.L.E. (2015),Action|Adventure|Comedy +139385,The Revenant (2015),Adventure|Drama +139644,Sicario (2015),Crime|Drama|Mystery +140110,The Intern (2015),Comedy +142488,Spotlight (2015),Thriller +143355,Wonder Woman (2017),Action|Adventure|Fantasy +148626,"Big Short, The (2015)",Drama +150548,Sherlock: The Abominable Bride (2016),Action|Crime|Drama|Mystery|Thriller +152077,10 Cloverfield Lane (2016),Thriller +152081,Zootopia (2016),Action|Adventure|Animation|Children|Comedy +157296,Finding Dory (2016),Adventure|Animation|Comedy +158238,The Nice Guys (2016),Crime|Mystery|Thriller +159093,Now You See Me 2 (2016),Action|Comedy|Thriller +164179,Arrival (2016),Sci-Fi +166461,Moana (2016),Adventure|Animation|Children|Comedy|Fantasy +166528,Rogue One: A Star Wars Story (2016),Action|Adventure|Fantasy|Sci-Fi +166643,Hidden Figures (2016),Drama +168250,Get Out (2017),Horror +168252,Logan (2017),Action|Sci-Fi +176371,Blade Runner 2049 (2017),Sci-Fi +177765,Coco (2017),Adventure|Animation|Children 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new file mode 100644 index 00000000..ff5bf5f3 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/images/movie_camera.png differ diff --git a/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/public_tests.py b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/public_tests.py new file mode 100644 index 00000000..306df393 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/public_tests.py @@ -0,0 +1,41 @@ +from tensorflow.keras.activations import relu, linear +from tensorflow.keras.layers import Dense + +import numpy as np + +def test_tower(target): + num_outputs = 32 + i = 0 + assert len(target.layers) == 3, f"Wrong number of layers. Expected 3 but got {len(target.layers)}" + expected = [[Dense, [None, 256], relu], + [Dense, [None, 128], relu], + [Dense, [None, num_outputs], linear]] + + for layer in target.layers: + assert type(layer) == expected[i][0], \ + f"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}" + assert layer.output.shape.as_list() == expected[i][1], \ + f"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}" + assert layer.activation == expected[i][2], \ + f"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}" + i = i + 1 + + print("\033[92mAll tests passed!") + + +def test_sq_dist(target): + a1 = np.array([1.0, 2.0, 3.0]); b1 = np.array([1.0, 2.0, 3.0]) + c1 = target(a1, b1) + a2 = np.array([1.1, 2.1, 3.1]); b2 = np.array([1.0, 2.0, 3.0]) + c2 = target(a2, b2) + a3 = np.array([0, 1]); b3 = np.array([1, 0]) + c3 = target(a3, b3) + a4 = np.array([1, 1, 1, 1, 1]); b4 = np.array([0, 0, 0, 0, 0]) + c4 = target(a4, b4) + + assert np.isclose(c1, 0), f"Wrong value. Expected {0}, got {c1}" + assert np.isclose(c2, 0.03), f"Wrong value. Expected {0.03}, got {c2}" + assert np.isclose(c3, 2), f"Wrong value. Expected {2}, got {c3}" + assert np.isclose(c4, 5), f"Wrong value. Expected {5}, got {c4}" + + print('\033[92mAll tests passed!') diff --git a/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/recsysNN_utils.py b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/recsysNN_utils.py new file mode 100644 index 00000000..65eb587d --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week2/5 Practice Lab 2/recsysNN_utils.py @@ -0,0 +1,282 @@ +import pickle5 as pickle +import numpy as np +from numpy import genfromtxt +from collections import defaultdict +import pandas as pd +import tensorflow as tf +from tensorflow.keras.models import Model +from sklearn.preprocessing import StandardScaler, MinMaxScaler +from sklearn.model_selection import train_test_split +import csv +import re +import tabulate + + +def load_data(): + item_train = genfromtxt('./data/content_item_train.csv', delimiter=',') + user_train = genfromtxt('./data/content_user_train.csv', delimiter=',') + y_train = genfromtxt('./data/content_y_train.csv', delimiter=',') + with open('./data/content_item_train_header.txt', newline='') as f: #csv reader handles quoted strings better + item_features = list(csv.reader(f))[0] + with open('./data/content_user_train_header.txt', newline='') as f: + user_features = list(csv.reader(f))[0] + item_vecs = genfromtxt('./data/content_item_vecs.csv', delimiter=',') + + movie_dict = defaultdict(dict) + count = 0 +# with open('./data/movies.csv', newline='') as csvfile: + with open('./data/content_movie_list.csv', newline='') as csvfile: + reader = csv.reader(csvfile, delimiter=',', quotechar='"') + for line in reader: + if count == 0: + count +=1 #skip header + #print(line) + else: + count +=1 + movie_id = int(line[0]) + movie_dict[movie_id]["title"] = line[1] + movie_dict[movie_id]["genres"] =line[2] + + with open('./data/content_user_to_genre.pickle', 'rb') as f: + user_to_genre = pickle.load(f) + + return(item_train, user_train, y_train, item_features, user_features, item_vecs, movie_dict, user_to_genre) + + +def pprint_train(x_train, features, vs, u_s, maxcount = 5, user=True): + """ Prints user_train or item_train nicely """ + if user: + flist = [".0f",".0f",".1f", + ".1f", ".1f", ".1f", ".1f",".1f",".1f", ".1f",".1f",".1f", ".1f",".1f",".1f",".1f",".1f"] + else: + flist = [".0f",".0f",".1f", + ".0f",".0f",".0f", ".0f",".0f",".0f", ".0f",".0f",".0f", ".0f",".0f",".0f",".0f",".0f"] + + head = features[:vs] + if vs < u_s: print("error, vector start {vs} should be greater then user start {u_s}") + for i in range(u_s): + head[i] = "[" + head[i] + "]" + genres = features[vs:] + hdr = head + genres + disp = [split_str(hdr, 5)] + count = 0 + for i in range(0,x_train.shape[0]): + if count == maxcount: break + count += 1 + disp.append( [ + x_train[i,0].astype(int), + x_train[i,1].astype(int), + x_train[i,2].astype(float), + *x_train[i,3:].astype(float) + ]) + table = tabulate.tabulate(disp, tablefmt='html',headers="firstrow", floatfmt=flist, numalign='center') + return(table) + + +def pprint_data(y_p, user_train, item_train, printfull=False): + np.set_printoptions(precision=1) + + for i in range(0,1000): + #print(f"{y_p[i,0]: 0.2f}, {ynorm_train.numpy()[i].item(): 0.2f}") + print(f"{y_pu[i,0]: 0.2f}, {y_train[i]: 0.2f}, ", end='') + print(f"{user_train[i,0].astype(int):d}, ", end='') # userid + print(f"{user_train[i,1].astype(int):d}, ", end=''), # rating cnt + print(f"{user_train[i,2].astype(float): 0.2f}, ", end='') # rating ave + print(": ", end = '') + print(f"{item_train[i,0].astype(int):d}, ", end='') # movie id + print(f"{item_train[i,2].astype(float):0.1f}, ", end='') # ave movie rating + if printfull: + for j in range(8, user_train.shape[1]): + print(f"{user_train[i,j].astype(float):0.1f}, ", end='') # rating + print(":", end='') + for j in range(3, item_train.shape[1]): + print(f"{item_train[i,j].astype(int):d}, ", end='') # rating + print() + else: + a = user_train[i, uvs:user_train.shape[1]] + b = item_train[i, ivs:item_train.shape[1]] + c = np.multiply(a,b) + print(c) + +def split_str(ifeatures, smax): + ofeatures = [] + for s in ifeatures: + if ' ' not in s: # skip string that already have a space + if len(s) > smax: + mid = int(len(s)/2) + s = s[:mid] + " " + s[mid:] + ofeatures.append(s) + return(ofeatures) + +def pprint_data_tab(y_p, user_train, item_train, uvs, ivs, user_features, item_features, maxcount = 20, printfull=False): + flist = [".1f", ".1f", ".0f", ".1f", ".0f", ".0f", ".0f", + ".1f",".1f",".1f",".1f",".1f",".1f",".1f",".1f",".1f",".1f",".1f",".1f",".1f",".1f"] + user_head = user_features[:uvs] + genres = user_features[uvs:] + item_head = item_features[:ivs] + hdr = ["y_p", "y"] + user_head + item_head + genres + disp = [split_str(hdr, 5)] + count = 0 + for i in range(0,y_p.shape[0]): + if count == maxcount: break + count += 1 + a = user_train[i, uvs:user_train.shape[1]] + b = item_train[i, ivs:item_train.shape[1]] + c = np.multiply(a,b) + + disp.append( [ y_p[i,0], y_train[i], + user_train[i,0].astype(int), # user id + user_train[i,1].astype(int), # rating cnt + user_train[i,2].astype(float), # user rating ave + item_train[i,0].astype(int), # movie id + item_train[i,1].astype(int), # year + item_train[i,2].astype(float), # ave movie rating + *c + ]) + table = tabulate.tabulate(disp, tablefmt='html',headers="firstrow", floatfmt=flist, numalign='center') + return(table) + + + + +def print_pred_movies(y_p, user, item, movie_dict, maxcount=10): + """ print results of prediction of a new user. inputs are expected to be in + sorted order, unscaled. """ + count = 0 + movies_listed = defaultdict(int) + disp = [["y_p", "movie id", "rating ave", "title", "genres"]] + + for i in range(0, y_p.shape[0]): + if count == maxcount: + break + count += 1 + movie_id = item[i, 0].astype(int) + if movie_id in movies_listed: + continue + movies_listed[movie_id] = 1 + disp.append([y_p[i, 0], item[i, 0].astype(int), item[i, 2].astype(float), + movie_dict[movie_id]['title'], movie_dict[movie_id]['genres']]) + + table = tabulate.tabulate(disp, tablefmt='html',headers="firstrow") + return(table) + +def gen_user_vecs(user_vec, num_items): + """ given a user vector return: + user predict maxtrix to match the size of item_vecs """ + user_vecs = np.tile(user_vec, (num_items, 1)) + return(user_vecs) + +# predict on everything, filter on print/use +def predict_uservec(user_vecs, item_vecs, model, u_s, i_s, scaler, ScalerUser, ScalerItem, scaledata=False): + """ given a user vector, does the prediction on all movies in item_vecs returns + an array predictions sorted by predicted rating, + arrays of user and item, sorted by predicted rating sorting index + """ + if scaledata: + scaled_user_vecs = ScalerUser.transform(user_vecs) + scaled_item_vecs = ScalerItem.transform(item_vecs) + y_p = model.predict([scaled_user_vecs[:, u_s:], scaled_item_vecs[:, i_s:]]) + else: + y_p = model.predict([user_vecs[:, u_s:], item_vecs[:, i_s:]]) + y_pu = scaler.inverse_transform(y_p) + + if np.any(y_pu < 0) : + print("Error, expected all positive predictions") + sorted_index = np.argsort(-y_pu,axis=0).reshape(-1).tolist() #negate to get largest rating first + sorted_ypu = y_pu[sorted_index] + sorted_items = item_vecs[sorted_index] + sorted_user = user_vecs[sorted_index] + return(sorted_index, sorted_ypu, sorted_items, sorted_user) + + +def print_pred_debug(y_p, y, user, item, maxcount=10, onlyrating=False, printfull=False): + """ hopefully reusable print. Keep for debug """ + count = 0 + for i in range(0, y_p.shape[0]): + if onlyrating == False or (onlyrating == True and y[i,0] != 0): + if count == maxcount: break + count += 1 + print(f"{y_p[i, 0]: 0.2f}, {y[i,0]: 0.2f}, ", end='') + print(f"{user[i, 0].astype(int):d}, ", end='') # userid + print(f"{user[i, 1].astype(int):d}, ", end=''), # rating cnt + print(f"{user[i, 2].astype(float):0.1f}, ", end=''), # rating ave + print(": ", end = '') + print(f"{item[i, 0].astype(int):d}, ", end='') # movie id + print(f"{item[i, 2].astype(float):0.1f}, ", end='') # ave movie rating + print(": ", end = '') + if printfull: + for j in range(uvs, user.shape[1]): + print(f"{user[i, j].astype(float):0.1f}, ", end='') # rating + print(":", end='') + for j in range(ivs, item.shape[1]): + print(f"{item[i, j].astype(int):d}, ", end='') # rating + print() + else: + a = user[i, uvs:user.shape[1]] + b = item[i, ivs:item.shape[1]] + c = np.multiply(a,b) + print(c) + + +def get_user_vecs(user_id, user_train, item_vecs, user_to_genre): + """ given a user_id, return: + user train/predict matrix to match the size of item_vecs + y vector with ratings for all rated movies and 0 for others of size item_vecs """ + + if user_id not in user_to_genre: + print("error: unknown user id") + return(None) + else: + user_vec_found = False + for i in range(len(user_train)): + if user_train[i, 0] == user_id: + user_vec = user_train[i] + user_vec_found = True + break + if not user_vec_found: + print("error in get_user_vecs, did not find uid in user_train") + num_items = len(item_vecs) + user_vecs = np.tile(user_vec, (num_items, 1)) + + y = np.zeros(num_items) + for i in range(num_items): # walk through movies in item_vecs and get the movies, see if user has rated them + movie_id = item_vecs[i, 0] + if movie_id in user_to_genre[user_id]['movies']: + rating = user_to_genre[user_id]['movies'][movie_id] + else: + rating = 0 + y[i] = rating + return(user_vecs, y) + + +def get_item_genre(item, ivs, item_features): + offset = np.where(item[ivs:] == 1)[0][0] + genre = item_features[ivs + offset] + return(genre, offset) + + +def print_existing_user(y_p, y, user, items, item_features, ivs, uvs, movie_dict, maxcount=10): + """ print results of prediction a user who was in the datatbase. inputs are expected to be in sorted order, unscaled. """ + count = 0 + movies_listed = defaultdict(int) + disp = [["y_p", "y", "user", "user genre ave", "movie rating ave", "title", "genres"]] + listed = [] + count = 0 + for i in range(0, y.shape[0]): + if y[i, 0] != 0: + if count == maxcount: + break + count += 1 + movie_id = items[i, 0].astype(int) + + offset = np.where(items[i, ivs:] == 1)[0][0] + genre_rating = user[i, uvs + offset] + genre = item_features[ivs + offset] + disp.append([y_p[i, 0], y[i, 0], + user[i, 0].astype(int), # userid + genre_rating.astype(float), + items[i, 2].astype(float), # movie average rating + movie_dict[movie_id]['title'], genre]) + + table = tabulate.tabulate(disp, tablefmt='html', headers="firstrow", floatfmt=[".1f", ".1f", ".0f", ".2f", ".2f"]) + return(table) \ No newline at end of file diff --git a/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/.ipynb_checkpoints/State-action value function example-checkpoint.ipynb b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/.ipynb_checkpoints/State-action value function example-checkpoint.ipynb new file mode 100644 index 00000000..a3de4cfa --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/.ipynb_checkpoints/State-action value function example-checkpoint.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# State Action Value Function Example\n", + "\n", + "In this Jupyter notebook, you can modify the mars rover example to see how the values of Q(s,a) will change depending on the rewards and discount factor changing." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Do not modify\n", + "num_states = 6\n", + "num_actions = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "terminal_left_reward = 100\n", + "terminal_right_reward = 40\n", + "each_step_reward = 0\n", + "\n", + "# Discount factor\n", + "gamma = 0.5\n", + "\n", + "# Probability of going in the wrong direction\n", + "misstep_prob = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "generate_visualization(terminal_left_reward, terminal_right_reward, each_step_reward, gamma, misstep_prob)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/State-action value function example.ipynb b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/State-action value function example.ipynb new file mode 100644 index 00000000..37f3d8be --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/State-action value function example.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# State Action Value Function Example\n", + "\n", + "In this Jupyter notebook, you can modify the mars rover example to see how the values of Q(s,a) will change depending on the rewards and discount factor changing." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from utils import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Do not modify\n", + "num_states = 6\n", + "num_actions = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "terminal_left_reward = 100\n", + "terminal_right_reward = 40\n", + "each_step_reward = 0\n", + "\n", + "# Discount factor\n", + "gamma = 0.5\n", + "\n", + "# Probability of going in the wrong direction\n", + "misstep_prob = 0.4" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "generate_visualization(terminal_left_reward, terminal_right_reward, each_step_reward, gamma, misstep_prob)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/__pycache__/utils.cpython-37.pyc b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/__pycache__/utils.cpython-37.pyc new file mode 100644 index 00000000..be485fb6 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/__pycache__/utils.cpython-37.pyc differ diff --git a/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/utils.py b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/utils.py new file mode 100644 index 00000000..02e74d48 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week3/2 State-action value function (optional lab)/utils.py @@ -0,0 +1,149 @@ +import numpy as np +import matplotlib.pyplot as plt + +def generate_rewards(num_states, each_step_reward, terminal_left_reward, terminal_right_reward): + + rewards = [each_step_reward] * num_states + rewards[0] = terminal_left_reward + rewards[-1] = terminal_right_reward + + return rewards + +def generate_transition_prob(num_states, num_actions, misstep_prob = 0): + # 0 is left, 1 is right + + p = np.zeros((num_states, num_actions, num_states)) + + for i in range(num_states): + if i != 0: + p[i, 0, i-1] = 1 - misstep_prob + p[i, 1, i-1] = misstep_prob + + if i != num_states - 1: + p[i, 1, i+1] = 1 - misstep_prob + p[i, 0, i+1] = misstep_prob + + # Terminal States + p[0] = np.zeros((num_actions, num_states)) + p[-1] = np.zeros((num_actions, num_states)) + + return p + +def calculate_Q_value(num_states, rewards, transition_prob, gamma, V_states, state, action): + q_sa = rewards[state] + gamma * sum([transition_prob[state, action, sp] * V_states[sp] for sp in range(num_states)]) + return q_sa + +def evaluate_policy(num_states, rewards, transition_prob, gamma, policy): + max_policy_eval = 10000 + threshold = 1e-10 + + V = np.zeros(num_states) + + for i in range(max_policy_eval): + delta = 0 + for s in range(num_states): + v = V[s] + V[s] = calculate_Q_value(num_states, rewards, transition_prob, gamma, V, s, policy[s]) + delta = max(delta, abs(v - V[s])) + + if delta < threshold: + break + + return V + +def improve_policy(num_states, num_actions, rewards, transition_prob, gamma, V, policy): + policy_stable = True + + for s in range(num_states): + q_best = V[s] + for a in range(num_actions): + q_sa = calculate_Q_value(num_states, rewards, transition_prob, gamma, V, s, a) + if q_sa > q_best and policy[s] != a: + policy[s] = a + q_best = q_sa + policy_stable = False + + return policy, policy_stable + + +def get_optimal_policy(num_states, num_actions, rewards, transition_prob, gamma): + optimal_policy = np.zeros(num_states, dtype=int) + max_policy_iter = 10000 + + for i in range(max_policy_iter): + policy_stable = True + + V = evaluate_policy(num_states, rewards, transition_prob, gamma, optimal_policy) + optimal_policy, policy_stable = improve_policy(num_states, num_actions, rewards, transition_prob, gamma, V, optimal_policy) + + if policy_stable: + break + + return optimal_policy, V + +def calculate_Q_values(num_states, rewards, transition_prob, gamma, optimal_policy): + # Left and then optimal policy + q_left_star = np.zeros(num_states) + + # Right and optimal policy + q_right_star = np.zeros(num_states) + + V_star = evaluate_policy(num_states, rewards, transition_prob, gamma, optimal_policy) + + for s in range(num_states): + q_left_star[s] = calculate_Q_value(num_states, rewards, transition_prob, gamma, V_star, s, 0) + q_right_star[s] = calculate_Q_value(num_states, rewards, transition_prob, gamma, V_star, s, 1) + + return q_left_star, q_right_star + + +def plot_optimal_policy_return(num_states, optimal_policy, rewards, V): + actions = [r"$\leftarrow$" if a == 0 else r"$\rightarrow$" for a in optimal_policy] + actions[0] = "" + actions[-1] = "" + + fig, ax = plt.subplots(figsize=(2*num_states,2)) + + for i in range(num_states): + ax.text(i+0.5, 0.5, actions[i], fontsize=32, ha="center", va="center", color="orange") + ax.text(i+0.5, 0.25, rewards[i], fontsize=16, ha="center", va="center", color="black") + ax.text(i+0.5, 0.75, round(V[i],2), fontsize=16, ha="center", va="center", color="firebrick") + ax.axvline(i, color="black") + ax.set_xlim([0, num_states]) + ax.set_ylim([0, 1]) + + ax.set_xticklabels([]) + ax.set_yticklabels([]) + ax.tick_params(axis='both', which='both', length=0) + ax.set_title("Optimal policy",fontsize = 16) + +def plot_q_values(num_states, q_left_star, q_right_star, rewards): + fig, ax = plt.subplots(figsize=(3*num_states,2)) + + for i in range(num_states): + ax.text(i+0.2, 0.6, round(q_left_star[i],2), fontsize=16, ha="center", va="center", color="firebrick") + ax.text(i+0.8, 0.6, round(q_right_star[i],2), fontsize=16, ha="center", va="center", color="firebrick") + + ax.text(i+0.5, 0.25, rewards[i], fontsize=20, ha="center", va="center", color="black") + ax.axvline(i, color="black") + ax.set_xlim([0, num_states]) + ax.set_ylim([0, 1]) + + ax.set_xticklabels([]) + ax.set_yticklabels([]) + ax.tick_params(axis='both', which='both', length=0) + ax.set_title("Q(s,a)",fontsize = 16) + +def generate_visualization(terminal_left_reward, terminal_right_reward, each_step_reward, gamma, misstep_prob): + num_states = 6 + num_actions = 2 + + rewards = generate_rewards(num_states, each_step_reward, terminal_left_reward, terminal_right_reward) + transition_prob = generate_transition_prob(num_states, num_actions, misstep_prob) + + optimal_policy, V = get_optimal_policy(num_states, num_actions, rewards, transition_prob, gamma) + q_left_star, q_right_star = calculate_Q_values(num_states, rewards, transition_prob, gamma, optimal_policy) + + plot_optimal_policy_return(num_states, optimal_policy, rewards, V) + plot_q_values(num_states, q_left_star, q_right_star, rewards) + \ No newline at end of file diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/.ipynb_checkpoints/C3_W3_A1_Assignment-checkpoint.ipynb b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/.ipynb_checkpoints/C3_W3_A1_Assignment-checkpoint.ipynb new file mode 100644 index 00000000..7102a214 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/.ipynb_checkpoints/C3_W3_A1_Assignment-checkpoint.ipynb @@ -0,0 +1,993 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Q-Learning - Lunar Lander\n", + "\n", + "In this assignment, you will train an agent to land a lunar lander safely on a landing pad on the surface of the moon.\n", + "\n", + "\n", + "# Outline\n", + "- [ 1 - Import Packages ](#1)\n", + "- [ 2 - Hyperparameters](#2)\n", + "- [ 3 - The Lunar Lander Environment](#3)\n", + " - [ 3.1 Action Space](#3.1)\n", + " - [ 3.2 Observation Space](#3.2)\n", + " - [ 3.3 Rewards](#3.3)\n", + " - [ 3.4 Episode Termination](#3.4)\n", + "- [ 4 - Load the Environment](#4)\n", + "- [ 5 - Interacting with the Gym Environment](#5)\n", + " - [ 5.1 Exploring the Environment's Dynamics](#5.1)\n", + "- [ 6 - Deep Q-Learning](#6)\n", + " - [ 6.1 Target Network](#6.1)\n", + " - [ Exercise 1](#ex01)\n", + " - [ 6.2 Experience Replay](#6.2)\n", + "- [ 7 - Deep Q-Learning Algorithm with Experience Replay](#7)\n", + " - [ Exercise 2](#ex02)\n", + "- [ 8 - Update the Network Weights](#8)\n", + "- [ 9 - Train the Agent](#9)\n", + "- [ 10 - See the Trained Agent In Action](#10)\n", + "- [ 11 - Congratulations!](#11)\n", + "- [ 12 - References](#12)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 - Import Packages\n", + "\n", + "We'll make use of the following packages:\n", + "- `numpy` is a package for scientific computing in python.\n", + "- `deque` will be our data structure for our memory buffer.\n", + "- `namedtuple` will be used to store the experience tuples.\n", + "- The `gym` toolkit is a collection of environments that can be used to test reinforcement learning algorithms. We should note that in this notebook we are using `gym` version `0.24.0`.\n", + "- `PIL.Image` and `pyvirtualdisplay` are needed to render the Lunar Lander environment.\n", + "- We will use several modules from the `tensorflow.keras` framework for building deep learning models.\n", + "- `utils` is a module that contains helper functions for this assignment. You do not need to modify the code in this file.\n", + "\n", + "Run the cell below to import all the necessary packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KYbOPKRtfQOr" + }, + "outputs": [], + "source": [ + "import time\n", + "from collections import deque, namedtuple\n", + "\n", + "import gym\n", + "import numpy as np\n", + "import PIL.Image\n", + "import tensorflow as tf\n", + "import utils\n", + "\n", + "from pyvirtualdisplay import Display\n", + "from tensorflow.keras import Sequential\n", + "from tensorflow.keras.layers import Dense, Input\n", + "from tensorflow.keras.losses import MSE\n", + "from tensorflow.keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up a virtual display to render the Lunar Lander environment.\n", + "Display(visible=0, size=(840, 480)).start();\n", + "\n", + "# Set the random seed for TensorFlow\n", + "tf.random.set_seed(utils.SEED)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2 - Hyperparameters\n", + "\n", + "Run the cell below to set the hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "MEMORY_SIZE = 100_000 # size of memory buffer\n", + "GAMMA = 0.995 # discount factor\n", + "ALPHA = 1e-3 # learning rate \n", + "NUM_STEPS_FOR_UPDATE = 4 # perform a learning update every C time steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 3 - The Lunar Lander Environment\n", + "\n", + "In this notebook we will be using [OpenAI's Gym Library](https://www.gymlibrary.ml/). The Gym library provides a wide variety of environments for reinforcement learning. To put it simply, an environment represents a problem or task to be solved. In this notebook, we will try to solve the Lunar Lander environment using reinforcement learning.\n", + "\n", + "The goal of the Lunar Lander environment is to land the lunar lander safely on the landing pad on the surface of the moon. The landing pad is designated by two flag poles and it is always at coordinates `(0,0)` but the lander is also allowed to land outside of the landing pad. The lander starts at the top center of the environment with a random initial force applied to its center of mass and has infinite fuel. The environment is considered solved if you get `200` points. \n", + "\n", + "
\n", + "
\n", + "
\n", + " \n", + "
Fig 1. Lunar Lander Environment.
\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "### 3.1 Action Space\n", + "\n", + "The agent has four discrete actions available:\n", + "\n", + "* Do nothing.\n", + "* Fire right engine.\n", + "* Fire main engine.\n", + "* Fire left engine.\n", + "\n", + "Each action has a corresponding numerical value:\n", + "\n", + "```python\n", + "Do nothing = 0\n", + "Fire right engine = 1\n", + "Fire main engine = 2\n", + "Fire left engine = 3\n", + "```\n", + "\n", + "\n", + "### 3.2 Observation Space\n", + "\n", + "The agent's observation space consists of a state vector with 8 variables:\n", + "\n", + "* Its $(x,y)$ coordinates. The landing pad is always at coordinates $(0,0)$.\n", + "* Its linear velocities $(\\dot x,\\dot y)$.\n", + "* Its angle $\\theta$.\n", + "* Its angular velocity $\\dot \\theta$.\n", + "* Two booleans, $l$ and $r$, that represent whether each leg is in contact with the ground or not.\n", + "\n", + "\n", + "### 3.3 Rewards\n", + "\n", + "The Lunar Lander environment has the following reward system:\n", + "\n", + "* Landing on the landing pad and coming to rest is about 100-140 points.\n", + "* If the lander moves away from the landing pad, it loses reward. \n", + "* If the lander crashes, it receives -100 points.\n", + "* If the lander comes to rest, it receives +100 points.\n", + "* Each leg with ground contact is +10 points.\n", + "* Firing the main engine is -0.3 points each frame.\n", + "* Firing the side engine is -0.03 points each frame.\n", + "\n", + "\n", + "### 3.4 Episode Termination\n", + "\n", + "An episode ends (i.e the environment enters a terminal state) if:\n", + "\n", + "* The lunar lander crashes (i.e if the body of the lunar lander comes in contact with the surface of the moon).\n", + "\n", + "* The lander's $x$-coordinate is greater than 1.\n", + "\n", + "You can check out the [Open AI Gym documentation](https://www.gymlibrary.ml/environments/box2d/lunar_lander/) for a full description of the environment. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4 - Load the Environment\n", + "\n", + "We start by loading the `LunarLander-v2` environment from the `gym` library by using the `.make()` method. `LunarLander-v2` is the latest version of the Lunar Lander environment and you can read about its version history in the [Open AI Gym documentation](https://www.gymlibrary.ml/environments/box2d/lunar_lander/#version-history)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ILVMYKewfR0n" + }, + "outputs": [], + "source": [ + "env = gym.make('LunarLander-v2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we load the environment we use the `.reset()` method to reset the environment to the initial state. The lander starts at the top center of the environment and we can render the first frame of the environment by using the `.render()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "env.reset()\n", + "PIL.Image.fromarray(env.render(mode='rgb_array'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to build our neural network later on we need to know the size of the state vector and the number of valid actions. We can get this information from our environment by using the `.observation_space.shape` and `action_space.n` methods, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x3fdqdG4CUu2" + }, + "outputs": [], + "source": [ + "state_size = env.observation_space.shape\n", + "num_actions = env.action_space.n\n", + "\n", + "print('State Shape:', state_size)\n", + "print('Number of actions:', num_actions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 5 - Interacting with the Gym Environment\n", + "\n", + "The Gym library implements the standard “agent-environment loop” formalism:\n", + "\n", + "
\n", + "
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Fig 2. Agent-environment Loop Formalism.
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\n", + "\n", + "In the standard “agent-environment loop” formalism, an agent interacts with the environment in discrete time steps $t=0,1,2,...$. At each time step $t$, the agent uses a policy $\\pi$ to select an action $A_t$ based on its observation of the environment's state $S_t$. The agent receives a numerical reward $R_t$ and on the next time step, moves to a new state $S_{t+1}$.\n", + "\n", + "\n", + "### 5.1 Exploring the Environment's Dynamics\n", + "\n", + "In Open AI's Gym environments, we use the `.step()` method to run a single time step of the environment's dynamics. In the version of `gym` that we are using the `.step()` method accepts an action and returns four values:\n", + "\n", + "* `observation` (**object**): an environment-specific object representing your observation of the environment. In the Lunar Lander environment this corresponds to a numpy array containing the positions and velocities of the lander as described in section [3.2 Observation Space](#3.2).\n", + "\n", + "\n", + "* `reward` (**float**): amount of reward returned as a result of taking the given action. In the Lunar Lander environment this corresponds to a float of type `numpy.float64` as described in section [3.3 Rewards](#3.3).\n", + "\n", + "\n", + "* `done` (**boolean**): When done is `True`, it indicates the episode has terminated and it’s time to reset the environment. \n", + "\n", + "\n", + "* `info` (**dictionary**): diagnostic information useful for debugging. We won't be using this variable in this notebook but it is shown here for completeness.\n", + "\n", + "To begin an episode, we need to reset the environment to an initial state. We do this by using the `.reset()` method. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reset the environment and get the initial state.\n", + "initial_state = env.reset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the environment is reset, the agent can start taking actions in the environment by using the `.step()` method. Note that the agent can only take one action per time step. \n", + "\n", + "In the cell below you can select different actions and see how the returned values change depending on the action taken. Remember that in this environment the agent has four discrete actions available and we specify them in code by using their corresponding numerical value:\n", + "\n", + "```python\n", + "Do nothing = 0\n", + "Fire right engine = 1\n", + "Fire main engine = 2\n", + "Fire left engine = 3\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select an action\n", + "action = 0\n", + "\n", + "# Run a single time step of the environment's dynamics with the given action.\n", + "next_state, reward, done, info = env.step(action)\n", + "\n", + "with np.printoptions(formatter={'float': '{:.3f}'.format}):\n", + " print(\"Initial State:\", initial_state)\n", + " print(\"Action:\", action)\n", + " print(\"Next State:\", next_state)\n", + " print(\"Reward Received:\", reward)\n", + " print(\"Episode Terminated:\", done)\n", + " print(\"Info:\", info)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In practice, when we train the agent we use a loop to allow the agent to take many consecutive actions during an episode." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 6 - Deep Q-Learning\n", + "\n", + "In cases where both the state and action space are discrete we can estimate the action-value function iteratively by using the Bellman equation:\n", + "\n", + "$$\n", + "Q_{i+1}(s,a) = R + \\gamma \\max_{a'}Q_i(s',a')\n", + "$$\n", + "\n", + "This iterative method converges to the optimal action-value function $Q^*(s,a)$ as $i\\to\\infty$. This means that the agent just needs to gradually explore the state-action space and keep updating the estimate of $Q(s,a)$ until it converges to the optimal action-value function $Q^*(s,a)$. However, in cases where the state space is continuous it becomes practically impossible to explore the entire state-action space. Consequently, this also makes it practically impossible to gradually estimate $Q(s,a)$ until it converges to $Q^*(s,a)$.\n", + "\n", + "In the Deep $Q$-Learning, we solve this problem by using a neural network to estimate the action-value function $Q(s,a)\\approx Q^*(s,a)$. We call this neural network a $Q$-Network and it can be trained by adjusting its weights at each iteration to minimize the mean-squared error in the Bellman equation.\n", + "\n", + "Unfortunately, using neural networks in reinforcement learning to estimate action-value functions has proven to be highly unstable. Luckily, there's a couple of techniques that can be employed to avoid instabilities. These techniques consist of using a ***Target Network*** and ***Experience Replay***. We will explore these two techniques in the following sections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 6.1 Target Network\n", + "\n", + "We can train the $Q$-Network by adjusting it's weights at each iteration to minimize the mean-squared error in the Bellman equation, where the target values are given by:\n", + "\n", + "$$\n", + "y = R + \\gamma \\max_{a'}Q(s',a';w)\n", + "$$\n", + "\n", + "where $w$ are the weights of the $Q$-Network. This means that we are adjusting the weights $w$ at each iteration to minimize the following error:\n", + "\n", + "$$\n", + "\\overbrace{\\underbrace{R + \\gamma \\max_{a'}Q(s',a'; w)}_{\\rm {y~target}} - Q(s,a;w)}^{\\rm {Error}}\n", + "$$\n", + "\n", + "Notice that this forms a problem because the $y$ target is changing on every iteration. Having a constantly moving target can lead to oscillations and instabilities. To avoid this, we can create\n", + "a separate neural network for generating the $y$ targets. We call this separate neural network the **target $\\hat Q$-Network** and it will have the same architecture as the original $Q$-Network. By using the target $\\hat Q$-Network, the above error becomes:\n", + "\n", + "$$\n", + "\\overbrace{\\underbrace{R + \\gamma \\max_{a'}\\hat{Q}(s',a'; w^-)}_{\\rm {y~target}} - Q(s,a;w)}^{\\rm {Error}}\n", + "$$\n", + "\n", + "where $w^-$ and $w$ are the weights the target $\\hat Q$-Network and $Q$-Network, respectively.\n", + "\n", + "In practice, we will use the following algorithm: every $C$ time steps we will use the $\\hat Q$-Network to generate the $y$ targets and update the weights of the target $\\hat Q$-Network using the weights of the $Q$-Network. We will update the weights $w^-$ of the the target $\\hat Q$-Network using a **soft update**. This means that we will update the weights $w^-$ using the following rule:\n", + " \n", + "$$\n", + "w^-\\leftarrow \\tau w + (1 - \\tau) w^-\n", + "$$\n", + "\n", + "where $\\tau\\ll 1$. By using the soft update, we are ensuring that the target values, $y$, change slowly, which greatly improves the stability of our learning algorithm." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 1\n", + "\n", + "In this exercise you will create the $Q$ and target $\\hat Q$ networks and set the optimizer. Remember that the Deep $Q$-Network (DQN) is a neural network that approximates the action-value function $Q(s,a)\\approx Q^*(s,a)$. It does this by learning how to map states to $Q$ values.\n", + "\n", + "To solve the Lunar Lander environment, we are going to employ a DQN with the following architecture:\n", + "\n", + "* An `Input` layer that takes `state_size` as input.\n", + "\n", + "* A `Dense` layer with `64` units and a `relu` activation function.\n", + "\n", + "* A `Dense` layer with `64` units and a `relu` activation function.\n", + "\n", + "* A `Dense` layer with `num_actions` units and a `linear` activation function. This will be the output layer of our network.\n", + "\n", + "\n", + "In the cell below you should create the $Q$-Network and the target $\\hat Q$-Network using the model architecture described above. Remember that both the $Q$-Network and the target $\\hat Q$-Network have the same architecture.\n", + "\n", + "Lastly, you should set `Adam` as the optimizer with a learning rate equal to `ALPHA`. Recall that `ALPHA` was defined in the [Hyperparameters](#2) section. We should note that for this exercise you should use the already imported packages:\n", + "```python\n", + "from tensorflow.keras.layers import Dense, Input\n", + "from tensorflow.keras.optimizers import Adam\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNQ_C1\n", + "# GRADED CELL\n", + "\n", + "# Create the Q-Network\n", + "q_network = Sequential([\n", + " ### START CODE HERE ### \n", + "\n", + " ### END CODE HERE ### \n", + " ])\n", + "\n", + "# Create the target Q^-Network\n", + "target_q_network = Sequential([\n", + " ### START CODE HERE ### \n", + "\n", + " ### END CODE HERE ###\n", + " ])\n", + "\n", + "### START CODE HERE ### \n", + "optimizer = None\n", + "### END CODE HERE ###" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNIT TEST\n", + "from public_tests import *\n", + "\n", + "test_network(q_network)\n", + "test_network(target_q_network)\n", + "test_optimizer(optimizer, ALPHA) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + "```python\n", + "# Create the Q-Network\n", + "q_network = Sequential([\n", + " Input(shape=state_size), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=num_actions, activation='linear'),\n", + " ])\n", + "\n", + "# Create the target Q^-Network\n", + "target_q_network = Sequential([\n", + " Input(shape=state_size), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=num_actions, activation='linear'), \n", + " ])\n", + "\n", + "optimizer = Adam(learning_rate=ALPHA) \n", + "``` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 6.2 Experience Replay\n", + "\n", + "When an agent interacts with the environment, the states, actions, and rewards the agent experiences are sequential by nature. If the agent tries to learn from these consecutive experiences it can run into problems due to the strong correlations between them. To avoid this, we employ a technique known as **Experience Replay** to generate uncorrelated experiences for training our agent. Experience replay consists of storing the agent's experiences (i.e the states, actions, and rewards the agent receives) in a memory buffer and then sampling a random mini-batch of experiences from the buffer to do the learning. The experience tuples $(S_t, A_t, R_t, S_{t+1})$ will be added to the memory buffer at each time step as the agent interacts with the environment.\n", + "\n", + "For convenience, we will store the experiences as named tuples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Store experiences as named tuples\n", + "experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By using experience replay we avoid problematic correlations, oscillations and instabilities. In addition, experience replay also allows the agent to potentially use the same experience in multiple weight updates, which increases data efficiency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7 - Deep Q-Learning Algorithm with Experience Replay\n", + "\n", + "Now that we know all the techniques that we are going to use, we can put them togther to arrive at the Deep Q-Learning Algorithm With Experience Replay.\n", + "
\n", + "
\n", + "
\n", + " \n", + "
Fig 3. Deep Q-Learning with Experience Replay.
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 2\n", + "\n", + "In this exercise you will implement line ***12*** of the algorithm outlined in *Fig 3* above and you will also compute the loss between the $y$ targets and the $Q(s,a)$ values. In the cell below, complete the `compute_loss` function by setting the $y$ targets equal to:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_j =\n", + " \\begin{cases}\n", + " R_j & \\text{if episode terminates at step } j+1\\\\\n", + " R_j + \\gamma \\max_{a'}\\hat{Q}(s_{j+1},a') & \\text{otherwise}\\\\\n", + " \\end{cases} \n", + "\\end{equation}\n", + "$$\n", + "\n", + "Here are a couple of things to note:\n", + "\n", + "* The `compute_loss` function takes in a mini-batch of experience tuples. This mini-batch of experience tuples is unpacked to extract the `states`, `actions`, `rewards`, `next_states`, and `done_vals`. You should keep in mind that these variables are *TensorFlow Tensors* whose size will depend on the mini-batch size. For example, if the mini-batch size is `64` then both `rewards` and `done_vals` will be TensorFlow Tensors with `64` elements.\n", + "\n", + "\n", + "* Using `if/else` statements to set the $y$ targets will not work when the variables are tensors with many elements. However, notice that you can use the `done_vals` to implement the above in a single line of code. To do this, recall that the `done` variable is a Boolean variable that takes the value `True` when an episode terminates at step $j+1$ and it is `False` otherwise. Taking into account that a Boolean value of `True` has the numerical value of `1` and a Boolean value of `False` has the numerical value of `0`, you can use the factor `(1 - done_vals)` to implement the above in a single line of code. Here's a hint: notice that `(1 - done_vals)` has a value of `0` when `done_vals` is `True` and a value of `1` when `done_vals` is `False`. \n", + "\n", + "Lastly, compute the loss by calculating the Mean-Squared Error (`MSE`) between the `y_targets` and the `q_values`. To calculate the mean-squared error you should use the already imported package `MSE`:\n", + "```python\n", + "from tensorflow.keras.losses import MSE\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNQ_C2\n", + "# GRADED FUNCTION: calculate_loss\n", + "\n", + "def compute_loss(experiences, gamma, q_network, target_q_network):\n", + " \"\"\" \n", + " Calculates the loss.\n", + " \n", + " Args:\n", + " experiences: (tuple) tuple of [\"state\", \"action\", \"reward\", \"next_state\", \"done\"] namedtuples\n", + " gamma: (float) The discount factor.\n", + " q_network: (tf.keras.Sequential) Keras model for predicting the q_values\n", + " target_q_network: (tf.keras.Sequential) Karas model for predicting the targets\n", + " \n", + " Returns:\n", + " loss: (TensorFlow Tensor(shape=(0,), dtype=int32)) the Mean-Squared Error between\n", + " the y targets and the Q(s,a) values.\n", + " \"\"\"\n", + " \n", + " # Unpack the mini-batch of experience tuples\n", + " states, actions, rewards, next_states, done_vals = experiences\n", + " \n", + " # Compute max Q^(s,a)\n", + " max_qsa = tf.reduce_max(target_q_network(next_states), axis=-1)\n", + " \n", + " # Set y = R if episode terminates, otherwise set y = R + γ max Q^(s,a).\n", + " ### START CODE HERE ### \n", + " y_targets = None\n", + " ### END CODE HERE ###\n", + " \n", + " # Get the q_values\n", + " q_values = q_network(states)\n", + " q_values = tf.gather_nd(q_values, tf.stack([tf.range(q_values.shape[0]),\n", + " tf.cast(actions, tf.int32)], axis=1))\n", + " \n", + " # Compute the loss\n", + " ### START CODE HERE ### \n", + " loss = None \n", + " ### END CODE HERE ### \n", + " \n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNIT TEST \n", + "test_compute_loss(compute_loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + "```python\n", + "def compute_loss(experiences, gamma, q_network, target_q_network):\n", + " \"\"\" \n", + " Calculates the loss.\n", + " \n", + " Args:\n", + " experiences: (tuple) tuple of [\"state\", \"action\", \"reward\", \"next_state\", \"done\"] namedtuples\n", + " gamma: (float) The discount factor.\n", + " q_network: (tf.keras.Sequential) Keras model for predicting the q_values\n", + " target_q_network: (tf.keras.Sequential) Karas model for predicting the targets\n", + " \n", + " Returns:\n", + " loss: (TensorFlow Tensor(shape=(0,), dtype=int32)) the Mean-Squared Error between\n", + " the y targets and the Q(s,a) values.\n", + " \"\"\"\n", + "\n", + " \n", + " # Unpack the mini-batch of experience tuples\n", + " states, actions, rewards, next_states, done_vals = experiences\n", + " \n", + " # Compute max Q^(s,a)\n", + " max_qsa = tf.reduce_max(target_q_network(next_states), axis=-1)\n", + " \n", + " # Set y = R if episode terminates, otherwise set y = R + γ max Q^(s,a).\n", + " y_targets = rewards + (gamma * max_qsa * (1 - done_vals))\n", + " \n", + " # Get the q_values\n", + " q_values = q_network(states)\n", + " q_values = tf.gather_nd(q_values, tf.stack([tf.range(q_values.shape[0]),\n", + " tf.cast(actions, tf.int32)], axis=1))\n", + " \n", + " # Calculate the loss\n", + " loss = MSE(y_targets, q_values)\n", + " \n", + " return loss\n", + "\n", + "``` \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 8 - Update the Network Weights\n", + "\n", + "We will use the `agent_learn` function below to implement lines ***12 -14*** of the algorithm outlined in [Fig 3](#7). The `agent_learn` function will update the weights of the $Q$ and target $\\hat Q$ networks using a custom training loop. Because we are using a custom training loop we need to retrieve the gradients via a `tf.GradientTape` instance, and then call `optimizer.apply_gradients()` to update the weights of our $Q$-Network. Note that we are also using the `@tf.function` decorator to increase performance. Without this decorator our training will take twice as long. If you would like to know more about how to increase performance with `@tf.function` take a look at the [TensorFlow documentation](https://www.tensorflow.org/guide/function).\n", + "\n", + "The last line of this function updates the weights of the target $\\hat Q$-Network using a [soft update](#6.1). If you want to know how this is implemented in code we encourage you to take a look at the `utils.update_target_network` function in the `utils` module." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@tf.function\n", + "def agent_learn(experiences, gamma):\n", + " \"\"\"\n", + " Updates the weights of the Q networks.\n", + " \n", + " Args:\n", + " experiences: (tuple) tuple of [\"state\", \"action\", \"reward\", \"next_state\", \"done\"] namedtuples\n", + " gamma: (float) The discount factor.\n", + " \n", + " \"\"\"\n", + " \n", + " # Calculate the loss\n", + " with tf.GradientTape() as tape:\n", + " loss = compute_loss(experiences, gamma, q_network, target_q_network)\n", + "\n", + " # Get the gradients of the loss with respect to the weights.\n", + " gradients = tape.gradient(loss, q_network.trainable_variables)\n", + " \n", + " # Update the weights of the q_network.\n", + " optimizer.apply_gradients(zip(gradients, q_network.trainable_variables))\n", + "\n", + " # update the weights of target q_network\n", + " utils.update_target_network(q_network, target_q_network)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 9 - Train the Agent\n", + "\n", + "We are now ready to train our agent to solve the Lunar Lander environment. In the cell below we will implement the algorithm in [Fig 3](#7) line by line (please note that we have included the same algorithm below for easy reference. This will prevent you from scrolling up and down the notebook):\n", + "\n", + "* **Line 1**: We initialize the `memory_buffer` with a capacity of $N =$ `MEMORY_SIZE`. Notice that we are using a `deque` as the data structure for our `memory_buffer`.\n", + "\n", + "\n", + "* **Line 2**: We skip this line since we already initialized the `q_network` in [Exercise 1](#ex01).\n", + "\n", + "\n", + "* **Line 3**: We initialize the `target_q_network` by setting its weights to be equal to those of the `q_network`.\n", + "\n", + "\n", + "* **Line 4**: We start the outer loop. Notice that we have set $M =$ `num_episodes = 2000`. This number is reasonable because the agent should be able to solve the Lunar Lander environment in less than `2000` episodes using this notebook's default parameters.\n", + "\n", + "\n", + "* **Line 5**: We use the `.reset()` method to reset the environment to the initial state and get the initial state.\n", + "\n", + "\n", + "* **Line 6**: We start the inner loop. Notice that we have set $T =$ `max_num_timesteps = 1000`. This means that the episode will automatically terminate if the episode hasn't terminated after `1000` time steps.\n", + "\n", + "\n", + "* **Line 7**: The agent observes the current `state` and chooses an `action` using an $\\epsilon$-greedy policy. Our agent starts out using a value of $\\epsilon =$ `epsilon = 1` which yields an $\\epsilon$-greedy policy that is equivalent to the equiprobable random policy. This means that at the beginning of our training, the agent is just going to take random actions regardless of the observed `state`. As training progresses we will decrease the value of $\\epsilon$ slowly towards a minimum value using a given $\\epsilon$-decay rate. We want this minimum value to be close to zero because a value of $\\epsilon = 0$ will yield an $\\epsilon$-greedy policy that is equivalent to the greedy policy. This means that towards the end of training, the agent will lean towards selecting the `action` that it believes (based on its past experiences) will maximize $Q(s,a)$. We will set the minimum $\\epsilon$ value to be `0.01` and not exactly 0 because we always want to keep a little bit of exploration during training. If you want to know how this is implemented in code we encourage you to take a look at the `utils.get_action` function in the `utils` module.\n", + "\n", + "\n", + "* **Line 8**: We use the `.step()` method to take the given `action` in the environment and get the `reward` and the `next_state`. \n", + "\n", + "\n", + "* **Line 9**: We store the `experience(state, action, reward, next_state, done)` tuple in our `memory_buffer`. Notice that we also store the `done` variable so that we can keep track of when an episode terminates. This allowed us to set the $y$ targets in [Exercise 2](#ex02).\n", + "\n", + "\n", + "* **Line 10**: We check if the conditions are met to perform a learning update. We do this by using our custom `utils.check_update_conditions` function. This function checks if $C =$ `NUM_STEPS_FOR_UPDATE = 4` time steps have occured and if our `memory_buffer` has enough experience tuples to fill a mini-batch. For example, if the mini-batch size is `64`, then our `memory_buffer` should have at least `64` experience tuples in order to pass the latter condition. If the conditions are met, then the `utils.check_update_conditions` function will return a value of `True`, otherwise it will return a value of `False`.\n", + "\n", + "\n", + "* **Lines 11 - 14**: If the `update` variable is `True` then we perform a learning update. The learning update consists of sampling a random mini-batch of experience tuples from our `memory_buffer`, setting the $y$ targets, performing gradient descent, and updating the weights of the networks. We will use the `agent_learn` function we defined in [Section 8](#8) to perform the latter 3.\n", + "\n", + "\n", + "* **Line 15**: At the end of each iteration of the inner loop we set `next_state` as our new `state` so that the loop can start again from this new state. In addition, we check if the episode has reached a terminal state (i.e we check if `done = True`). If a terminal state has been reached, then we break out of the inner loop.\n", + "\n", + "\n", + "* **Line 16**: At the end of each iteration of the outer loop we update the value of $\\epsilon$, and check if the environment has been solved. We consider that the environment has been solved if the agent receives an average of `200` points in the last `100` episodes. If the environment has not been solved we continue the outer loop and start a new episode.\n", + "\n", + "Finally, we wanted to note that we have included some extra variables to keep track of the total number of points the agent received in each episode. This will help us determine if the agent has solved the environment and it will also allow us to see how our agent performed during training. We also use the `time` module to measure how long the training takes. \n", + "\n", + "
\n", + "
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\n", + " \n", + "
Fig 4. Deep Q-Learning with Experience Replay.
\n", + "
\n", + "
\n", + "\n", + "**Note:** With this notebook's default parameters, the following cell takes between 10 to 15 minutes to run. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start = time.time()\n", + "\n", + "num_episodes = 2000\n", + "max_num_timesteps = 1000\n", + "\n", + "total_point_history = []\n", + "\n", + "num_p_av = 100 # number of total points to use for averaging\n", + "epsilon = 1.0 # initial ε value for ε-greedy policy\n", + "\n", + "# Create a memory buffer D with capacity N\n", + "memory_buffer = deque(maxlen=MEMORY_SIZE)\n", + "\n", + "# Set the target network weights equal to the Q-Network weights\n", + "target_q_network.set_weights(q_network.get_weights())\n", + "\n", + "for i in range(num_episodes):\n", + " \n", + " # Reset the environment to the initial state and get the initial state\n", + " state = env.reset()\n", + " total_points = 0\n", + " \n", + " for t in range(max_num_timesteps):\n", + " \n", + " # From the current state S choose an action A using an ε-greedy policy\n", + " state_qn = np.expand_dims(state, axis=0) # state needs to be the right shape for the q_network\n", + " q_values = q_network(state_qn)\n", + " action = utils.get_action(q_values, epsilon)\n", + " \n", + " # Take action A and receive reward R and the next state S'\n", + " next_state, reward, done, _ = env.step(action)\n", + " \n", + " # Store experience tuple (S,A,R,S') in the memory buffer.\n", + " # We store the done variable as well for convenience.\n", + " memory_buffer.append(experience(state, action, reward, next_state, done))\n", + " \n", + " # Only update the network every NUM_STEPS_FOR_UPDATE time steps.\n", + " update = utils.check_update_conditions(t, NUM_STEPS_FOR_UPDATE, memory_buffer)\n", + " \n", + " if update:\n", + " # Sample random mini-batch of experience tuples (S,A,R,S') from D\n", + " experiences = utils.get_experiences(memory_buffer)\n", + " \n", + " # Set the y targets, perform a gradient descent step,\n", + " # and update the network weights.\n", + " agent_learn(experiences, GAMMA)\n", + " \n", + " state = next_state.copy()\n", + " total_points += reward\n", + " \n", + " if done:\n", + " break\n", + " \n", + " total_point_history.append(total_points)\n", + " av_latest_points = np.mean(total_point_history[-num_p_av:])\n", + " \n", + " # Update the ε value\n", + " epsilon = utils.get_new_eps(epsilon)\n", + "\n", + " print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\", end=\"\")\n", + "\n", + " if (i+1) % num_p_av == 0:\n", + " print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\")\n", + "\n", + " # We will consider that the environment is solved if we get an\n", + " # average of 200 points in the last 100 episodes.\n", + " if av_latest_points >= 200.0:\n", + " print(f\"\\n\\nEnvironment solved in {i+1} episodes!\")\n", + " q_network.save('lunar_lander_model.h5')\n", + " break\n", + " \n", + "tot_time = time.time() - start\n", + "\n", + "print(f\"\\nTotal Runtime: {tot_time:.2f} s ({(tot_time/60):.2f} min)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the point history to see how our agent improved during training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E_EUXxurfe8m", + "scrolled": false + }, + "outputs": [], + "source": [ + "# Plot the point history\n", + "utils.plot_history(total_point_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c_xwgaX5MnYt" + }, + "source": [ + "\n", + "## 10 - See the Trained Agent In Action\n", + "\n", + "Now that we have trained our agent, we can see it in action. We will use the `utils.create_video` function to create a video of our agent interacting with the environment using the trained $Q$-Network. The `utils.create_video` function uses the `imageio` library to create the video. This library produces some warnings that can be distracting, so, to suppress these warnings we run the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Suppress warnings from imageio\n", + "import logging\n", + "logging.getLogger().setLevel(logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below we create a video of our agent interacting with the Lunar Lander environment using the trained `q_network`. The video is saved to the `videos` folder with the given `filename`. We use the `utils.embed_mp4` function to embed the video in the Jupyter Notebook so that we can see it here directly without having to download it.\n", + "\n", + "We should note that since the lunar lander starts with a random initial force applied to its center of mass, every time you run the cell below you will see a different video. If the agent was trained properly, it should be able to land the lunar lander in the landing pad every time, regardless of the initial force applied to its center of mass." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3Ttb_zLeJKiG" + }, + "outputs": [], + "source": [ + "filename = \"./videos/lunar_lander.mp4\"\n", + "\n", + "utils.create_video(filename, env, q_network)\n", + "utils.embed_mp4(filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 11 - Congratulations!\n", + "\n", + "You have successfully used Deep Q-Learning with Experience Replay to train an agent to land a lunar lander safely on a landing pad on the surface of the moon. Congratulations!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12 - References\n", + "\n", + "If you would like to learn more about Deep Q-Learning, we recommend you check out the following papers.\n", + "\n", + "\n", + "* [Human-level Control Through Deep Reinforcement Learning](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)\n", + "\n", + "\n", + "* [Continuous Control with Deep Reinforcement Learning](https://arxiv.org/pdf/1509.02971.pdf)\n", + "\n", + "\n", + "* [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "TensorFlow - Lunar Lander.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/C3_W3_A1_Assignment.ipynb b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/C3_W3_A1_Assignment.ipynb new file mode 100644 index 00000000..7102a214 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/C3_W3_A1_Assignment.ipynb @@ -0,0 +1,993 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Q-Learning - Lunar Lander\n", + "\n", + "In this assignment, you will train an agent to land a lunar lander safely on a landing pad on the surface of the moon.\n", + "\n", + "\n", + "# Outline\n", + "- [ 1 - Import Packages ](#1)\n", + "- [ 2 - Hyperparameters](#2)\n", + "- [ 3 - The Lunar Lander Environment](#3)\n", + " - [ 3.1 Action Space](#3.1)\n", + " - [ 3.2 Observation Space](#3.2)\n", + " - [ 3.3 Rewards](#3.3)\n", + " - [ 3.4 Episode Termination](#3.4)\n", + "- [ 4 - Load the Environment](#4)\n", + "- [ 5 - Interacting with the Gym Environment](#5)\n", + " - [ 5.1 Exploring the Environment's Dynamics](#5.1)\n", + "- [ 6 - Deep Q-Learning](#6)\n", + " - [ 6.1 Target Network](#6.1)\n", + " - [ Exercise 1](#ex01)\n", + " - [ 6.2 Experience Replay](#6.2)\n", + "- [ 7 - Deep Q-Learning Algorithm with Experience Replay](#7)\n", + " - [ Exercise 2](#ex02)\n", + "- [ 8 - Update the Network Weights](#8)\n", + "- [ 9 - Train the Agent](#9)\n", + "- [ 10 - See the Trained Agent In Action](#10)\n", + "- [ 11 - Congratulations!](#11)\n", + "- [ 12 - References](#12)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 1 - Import Packages\n", + "\n", + "We'll make use of the following packages:\n", + "- `numpy` is a package for scientific computing in python.\n", + "- `deque` will be our data structure for our memory buffer.\n", + "- `namedtuple` will be used to store the experience tuples.\n", + "- The `gym` toolkit is a collection of environments that can be used to test reinforcement learning algorithms. We should note that in this notebook we are using `gym` version `0.24.0`.\n", + "- `PIL.Image` and `pyvirtualdisplay` are needed to render the Lunar Lander environment.\n", + "- We will use several modules from the `tensorflow.keras` framework for building deep learning models.\n", + "- `utils` is a module that contains helper functions for this assignment. You do not need to modify the code in this file.\n", + "\n", + "Run the cell below to import all the necessary packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KYbOPKRtfQOr" + }, + "outputs": [], + "source": [ + "import time\n", + "from collections import deque, namedtuple\n", + "\n", + "import gym\n", + "import numpy as np\n", + "import PIL.Image\n", + "import tensorflow as tf\n", + "import utils\n", + "\n", + "from pyvirtualdisplay import Display\n", + "from tensorflow.keras import Sequential\n", + "from tensorflow.keras.layers import Dense, Input\n", + "from tensorflow.keras.losses import MSE\n", + "from tensorflow.keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up a virtual display to render the Lunar Lander environment.\n", + "Display(visible=0, size=(840, 480)).start();\n", + "\n", + "# Set the random seed for TensorFlow\n", + "tf.random.set_seed(utils.SEED)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2 - Hyperparameters\n", + "\n", + "Run the cell below to set the hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "MEMORY_SIZE = 100_000 # size of memory buffer\n", + "GAMMA = 0.995 # discount factor\n", + "ALPHA = 1e-3 # learning rate \n", + "NUM_STEPS_FOR_UPDATE = 4 # perform a learning update every C time steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 3 - The Lunar Lander Environment\n", + "\n", + "In this notebook we will be using [OpenAI's Gym Library](https://www.gymlibrary.ml/). The Gym library provides a wide variety of environments for reinforcement learning. To put it simply, an environment represents a problem or task to be solved. In this notebook, we will try to solve the Lunar Lander environment using reinforcement learning.\n", + "\n", + "The goal of the Lunar Lander environment is to land the lunar lander safely on the landing pad on the surface of the moon. The landing pad is designated by two flag poles and it is always at coordinates `(0,0)` but the lander is also allowed to land outside of the landing pad. The lander starts at the top center of the environment with a random initial force applied to its center of mass and has infinite fuel. The environment is considered solved if you get `200` points. \n", + "\n", + "
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Fig 1. Lunar Lander Environment.
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\n", + "\n", + "\n", + "\n", + "\n", + "### 3.1 Action Space\n", + "\n", + "The agent has four discrete actions available:\n", + "\n", + "* Do nothing.\n", + "* Fire right engine.\n", + "* Fire main engine.\n", + "* Fire left engine.\n", + "\n", + "Each action has a corresponding numerical value:\n", + "\n", + "```python\n", + "Do nothing = 0\n", + "Fire right engine = 1\n", + "Fire main engine = 2\n", + "Fire left engine = 3\n", + "```\n", + "\n", + "\n", + "### 3.2 Observation Space\n", + "\n", + "The agent's observation space consists of a state vector with 8 variables:\n", + "\n", + "* Its $(x,y)$ coordinates. The landing pad is always at coordinates $(0,0)$.\n", + "* Its linear velocities $(\\dot x,\\dot y)$.\n", + "* Its angle $\\theta$.\n", + "* Its angular velocity $\\dot \\theta$.\n", + "* Two booleans, $l$ and $r$, that represent whether each leg is in contact with the ground or not.\n", + "\n", + "\n", + "### 3.3 Rewards\n", + "\n", + "The Lunar Lander environment has the following reward system:\n", + "\n", + "* Landing on the landing pad and coming to rest is about 100-140 points.\n", + "* If the lander moves away from the landing pad, it loses reward. \n", + "* If the lander crashes, it receives -100 points.\n", + "* If the lander comes to rest, it receives +100 points.\n", + "* Each leg with ground contact is +10 points.\n", + "* Firing the main engine is -0.3 points each frame.\n", + "* Firing the side engine is -0.03 points each frame.\n", + "\n", + "\n", + "### 3.4 Episode Termination\n", + "\n", + "An episode ends (i.e the environment enters a terminal state) if:\n", + "\n", + "* The lunar lander crashes (i.e if the body of the lunar lander comes in contact with the surface of the moon).\n", + "\n", + "* The lander's $x$-coordinate is greater than 1.\n", + "\n", + "You can check out the [Open AI Gym documentation](https://www.gymlibrary.ml/environments/box2d/lunar_lander/) for a full description of the environment. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4 - Load the Environment\n", + "\n", + "We start by loading the `LunarLander-v2` environment from the `gym` library by using the `.make()` method. `LunarLander-v2` is the latest version of the Lunar Lander environment and you can read about its version history in the [Open AI Gym documentation](https://www.gymlibrary.ml/environments/box2d/lunar_lander/#version-history)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ILVMYKewfR0n" + }, + "outputs": [], + "source": [ + "env = gym.make('LunarLander-v2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we load the environment we use the `.reset()` method to reset the environment to the initial state. The lander starts at the top center of the environment and we can render the first frame of the environment by using the `.render()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "env.reset()\n", + "PIL.Image.fromarray(env.render(mode='rgb_array'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to build our neural network later on we need to know the size of the state vector and the number of valid actions. We can get this information from our environment by using the `.observation_space.shape` and `action_space.n` methods, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x3fdqdG4CUu2" + }, + "outputs": [], + "source": [ + "state_size = env.observation_space.shape\n", + "num_actions = env.action_space.n\n", + "\n", + "print('State Shape:', state_size)\n", + "print('Number of actions:', num_actions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 5 - Interacting with the Gym Environment\n", + "\n", + "The Gym library implements the standard “agent-environment loop” formalism:\n", + "\n", + "
\n", + "
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Fig 2. Agent-environment Loop Formalism.
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\n", + "\n", + "In the standard “agent-environment loop” formalism, an agent interacts with the environment in discrete time steps $t=0,1,2,...$. At each time step $t$, the agent uses a policy $\\pi$ to select an action $A_t$ based on its observation of the environment's state $S_t$. The agent receives a numerical reward $R_t$ and on the next time step, moves to a new state $S_{t+1}$.\n", + "\n", + "\n", + "### 5.1 Exploring the Environment's Dynamics\n", + "\n", + "In Open AI's Gym environments, we use the `.step()` method to run a single time step of the environment's dynamics. In the version of `gym` that we are using the `.step()` method accepts an action and returns four values:\n", + "\n", + "* `observation` (**object**): an environment-specific object representing your observation of the environment. In the Lunar Lander environment this corresponds to a numpy array containing the positions and velocities of the lander as described in section [3.2 Observation Space](#3.2).\n", + "\n", + "\n", + "* `reward` (**float**): amount of reward returned as a result of taking the given action. In the Lunar Lander environment this corresponds to a float of type `numpy.float64` as described in section [3.3 Rewards](#3.3).\n", + "\n", + "\n", + "* `done` (**boolean**): When done is `True`, it indicates the episode has terminated and it’s time to reset the environment. \n", + "\n", + "\n", + "* `info` (**dictionary**): diagnostic information useful for debugging. We won't be using this variable in this notebook but it is shown here for completeness.\n", + "\n", + "To begin an episode, we need to reset the environment to an initial state. We do this by using the `.reset()` method. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reset the environment and get the initial state.\n", + "initial_state = env.reset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the environment is reset, the agent can start taking actions in the environment by using the `.step()` method. Note that the agent can only take one action per time step. \n", + "\n", + "In the cell below you can select different actions and see how the returned values change depending on the action taken. Remember that in this environment the agent has four discrete actions available and we specify them in code by using their corresponding numerical value:\n", + "\n", + "```python\n", + "Do nothing = 0\n", + "Fire right engine = 1\n", + "Fire main engine = 2\n", + "Fire left engine = 3\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select an action\n", + "action = 0\n", + "\n", + "# Run a single time step of the environment's dynamics with the given action.\n", + "next_state, reward, done, info = env.step(action)\n", + "\n", + "with np.printoptions(formatter={'float': '{:.3f}'.format}):\n", + " print(\"Initial State:\", initial_state)\n", + " print(\"Action:\", action)\n", + " print(\"Next State:\", next_state)\n", + " print(\"Reward Received:\", reward)\n", + " print(\"Episode Terminated:\", done)\n", + " print(\"Info:\", info)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In practice, when we train the agent we use a loop to allow the agent to take many consecutive actions during an episode." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 6 - Deep Q-Learning\n", + "\n", + "In cases where both the state and action space are discrete we can estimate the action-value function iteratively by using the Bellman equation:\n", + "\n", + "$$\n", + "Q_{i+1}(s,a) = R + \\gamma \\max_{a'}Q_i(s',a')\n", + "$$\n", + "\n", + "This iterative method converges to the optimal action-value function $Q^*(s,a)$ as $i\\to\\infty$. This means that the agent just needs to gradually explore the state-action space and keep updating the estimate of $Q(s,a)$ until it converges to the optimal action-value function $Q^*(s,a)$. However, in cases where the state space is continuous it becomes practically impossible to explore the entire state-action space. Consequently, this also makes it practically impossible to gradually estimate $Q(s,a)$ until it converges to $Q^*(s,a)$.\n", + "\n", + "In the Deep $Q$-Learning, we solve this problem by using a neural network to estimate the action-value function $Q(s,a)\\approx Q^*(s,a)$. We call this neural network a $Q$-Network and it can be trained by adjusting its weights at each iteration to minimize the mean-squared error in the Bellman equation.\n", + "\n", + "Unfortunately, using neural networks in reinforcement learning to estimate action-value functions has proven to be highly unstable. Luckily, there's a couple of techniques that can be employed to avoid instabilities. These techniques consist of using a ***Target Network*** and ***Experience Replay***. We will explore these two techniques in the following sections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 6.1 Target Network\n", + "\n", + "We can train the $Q$-Network by adjusting it's weights at each iteration to minimize the mean-squared error in the Bellman equation, where the target values are given by:\n", + "\n", + "$$\n", + "y = R + \\gamma \\max_{a'}Q(s',a';w)\n", + "$$\n", + "\n", + "where $w$ are the weights of the $Q$-Network. This means that we are adjusting the weights $w$ at each iteration to minimize the following error:\n", + "\n", + "$$\n", + "\\overbrace{\\underbrace{R + \\gamma \\max_{a'}Q(s',a'; w)}_{\\rm {y~target}} - Q(s,a;w)}^{\\rm {Error}}\n", + "$$\n", + "\n", + "Notice that this forms a problem because the $y$ target is changing on every iteration. Having a constantly moving target can lead to oscillations and instabilities. To avoid this, we can create\n", + "a separate neural network for generating the $y$ targets. We call this separate neural network the **target $\\hat Q$-Network** and it will have the same architecture as the original $Q$-Network. By using the target $\\hat Q$-Network, the above error becomes:\n", + "\n", + "$$\n", + "\\overbrace{\\underbrace{R + \\gamma \\max_{a'}\\hat{Q}(s',a'; w^-)}_{\\rm {y~target}} - Q(s,a;w)}^{\\rm {Error}}\n", + "$$\n", + "\n", + "where $w^-$ and $w$ are the weights the target $\\hat Q$-Network and $Q$-Network, respectively.\n", + "\n", + "In practice, we will use the following algorithm: every $C$ time steps we will use the $\\hat Q$-Network to generate the $y$ targets and update the weights of the target $\\hat Q$-Network using the weights of the $Q$-Network. We will update the weights $w^-$ of the the target $\\hat Q$-Network using a **soft update**. This means that we will update the weights $w^-$ using the following rule:\n", + " \n", + "$$\n", + "w^-\\leftarrow \\tau w + (1 - \\tau) w^-\n", + "$$\n", + "\n", + "where $\\tau\\ll 1$. By using the soft update, we are ensuring that the target values, $y$, change slowly, which greatly improves the stability of our learning algorithm." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 1\n", + "\n", + "In this exercise you will create the $Q$ and target $\\hat Q$ networks and set the optimizer. Remember that the Deep $Q$-Network (DQN) is a neural network that approximates the action-value function $Q(s,a)\\approx Q^*(s,a)$. It does this by learning how to map states to $Q$ values.\n", + "\n", + "To solve the Lunar Lander environment, we are going to employ a DQN with the following architecture:\n", + "\n", + "* An `Input` layer that takes `state_size` as input.\n", + "\n", + "* A `Dense` layer with `64` units and a `relu` activation function.\n", + "\n", + "* A `Dense` layer with `64` units and a `relu` activation function.\n", + "\n", + "* A `Dense` layer with `num_actions` units and a `linear` activation function. This will be the output layer of our network.\n", + "\n", + "\n", + "In the cell below you should create the $Q$-Network and the target $\\hat Q$-Network using the model architecture described above. Remember that both the $Q$-Network and the target $\\hat Q$-Network have the same architecture.\n", + "\n", + "Lastly, you should set `Adam` as the optimizer with a learning rate equal to `ALPHA`. Recall that `ALPHA` was defined in the [Hyperparameters](#2) section. We should note that for this exercise you should use the already imported packages:\n", + "```python\n", + "from tensorflow.keras.layers import Dense, Input\n", + "from tensorflow.keras.optimizers import Adam\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNQ_C1\n", + "# GRADED CELL\n", + "\n", + "# Create the Q-Network\n", + "q_network = Sequential([\n", + " ### START CODE HERE ### \n", + "\n", + " ### END CODE HERE ### \n", + " ])\n", + "\n", + "# Create the target Q^-Network\n", + "target_q_network = Sequential([\n", + " ### START CODE HERE ### \n", + "\n", + " ### END CODE HERE ###\n", + " ])\n", + "\n", + "### START CODE HERE ### \n", + "optimizer = None\n", + "### END CODE HERE ###" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNIT TEST\n", + "from public_tests import *\n", + "\n", + "test_network(q_network)\n", + "test_network(target_q_network)\n", + "test_optimizer(optimizer, ALPHA) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + "```python\n", + "# Create the Q-Network\n", + "q_network = Sequential([\n", + " Input(shape=state_size), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=num_actions, activation='linear'),\n", + " ])\n", + "\n", + "# Create the target Q^-Network\n", + "target_q_network = Sequential([\n", + " Input(shape=state_size), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=64, activation='relu'), \n", + " Dense(units=num_actions, activation='linear'), \n", + " ])\n", + "\n", + "optimizer = Adam(learning_rate=ALPHA) \n", + "``` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 6.2 Experience Replay\n", + "\n", + "When an agent interacts with the environment, the states, actions, and rewards the agent experiences are sequential by nature. If the agent tries to learn from these consecutive experiences it can run into problems due to the strong correlations between them. To avoid this, we employ a technique known as **Experience Replay** to generate uncorrelated experiences for training our agent. Experience replay consists of storing the agent's experiences (i.e the states, actions, and rewards the agent receives) in a memory buffer and then sampling a random mini-batch of experiences from the buffer to do the learning. The experience tuples $(S_t, A_t, R_t, S_{t+1})$ will be added to the memory buffer at each time step as the agent interacts with the environment.\n", + "\n", + "For convenience, we will store the experiences as named tuples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Store experiences as named tuples\n", + "experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By using experience replay we avoid problematic correlations, oscillations and instabilities. In addition, experience replay also allows the agent to potentially use the same experience in multiple weight updates, which increases data efficiency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7 - Deep Q-Learning Algorithm with Experience Replay\n", + "\n", + "Now that we know all the techniques that we are going to use, we can put them togther to arrive at the Deep Q-Learning Algorithm With Experience Replay.\n", + "
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Fig 3. Deep Q-Learning with Experience Replay.
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 2\n", + "\n", + "In this exercise you will implement line ***12*** of the algorithm outlined in *Fig 3* above and you will also compute the loss between the $y$ targets and the $Q(s,a)$ values. In the cell below, complete the `compute_loss` function by setting the $y$ targets equal to:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_j =\n", + " \\begin{cases}\n", + " R_j & \\text{if episode terminates at step } j+1\\\\\n", + " R_j + \\gamma \\max_{a'}\\hat{Q}(s_{j+1},a') & \\text{otherwise}\\\\\n", + " \\end{cases} \n", + "\\end{equation}\n", + "$$\n", + "\n", + "Here are a couple of things to note:\n", + "\n", + "* The `compute_loss` function takes in a mini-batch of experience tuples. This mini-batch of experience tuples is unpacked to extract the `states`, `actions`, `rewards`, `next_states`, and `done_vals`. You should keep in mind that these variables are *TensorFlow Tensors* whose size will depend on the mini-batch size. For example, if the mini-batch size is `64` then both `rewards` and `done_vals` will be TensorFlow Tensors with `64` elements.\n", + "\n", + "\n", + "* Using `if/else` statements to set the $y$ targets will not work when the variables are tensors with many elements. However, notice that you can use the `done_vals` to implement the above in a single line of code. To do this, recall that the `done` variable is a Boolean variable that takes the value `True` when an episode terminates at step $j+1$ and it is `False` otherwise. Taking into account that a Boolean value of `True` has the numerical value of `1` and a Boolean value of `False` has the numerical value of `0`, you can use the factor `(1 - done_vals)` to implement the above in a single line of code. Here's a hint: notice that `(1 - done_vals)` has a value of `0` when `done_vals` is `True` and a value of `1` when `done_vals` is `False`. \n", + "\n", + "Lastly, compute the loss by calculating the Mean-Squared Error (`MSE`) between the `y_targets` and the `q_values`. To calculate the mean-squared error you should use the already imported package `MSE`:\n", + "```python\n", + "from tensorflow.keras.losses import MSE\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNQ_C2\n", + "# GRADED FUNCTION: calculate_loss\n", + "\n", + "def compute_loss(experiences, gamma, q_network, target_q_network):\n", + " \"\"\" \n", + " Calculates the loss.\n", + " \n", + " Args:\n", + " experiences: (tuple) tuple of [\"state\", \"action\", \"reward\", \"next_state\", \"done\"] namedtuples\n", + " gamma: (float) The discount factor.\n", + " q_network: (tf.keras.Sequential) Keras model for predicting the q_values\n", + " target_q_network: (tf.keras.Sequential) Karas model for predicting the targets\n", + " \n", + " Returns:\n", + " loss: (TensorFlow Tensor(shape=(0,), dtype=int32)) the Mean-Squared Error between\n", + " the y targets and the Q(s,a) values.\n", + " \"\"\"\n", + " \n", + " # Unpack the mini-batch of experience tuples\n", + " states, actions, rewards, next_states, done_vals = experiences\n", + " \n", + " # Compute max Q^(s,a)\n", + " max_qsa = tf.reduce_max(target_q_network(next_states), axis=-1)\n", + " \n", + " # Set y = R if episode terminates, otherwise set y = R + γ max Q^(s,a).\n", + " ### START CODE HERE ### \n", + " y_targets = None\n", + " ### END CODE HERE ###\n", + " \n", + " # Get the q_values\n", + " q_values = q_network(states)\n", + " q_values = tf.gather_nd(q_values, tf.stack([tf.range(q_values.shape[0]),\n", + " tf.cast(actions, tf.int32)], axis=1))\n", + " \n", + " # Compute the loss\n", + " ### START CODE HERE ### \n", + " loss = None \n", + " ### END CODE HERE ### \n", + " \n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# UNIT TEST \n", + "test_compute_loss(compute_loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " Click for hints\n", + " \n", + "```python\n", + "def compute_loss(experiences, gamma, q_network, target_q_network):\n", + " \"\"\" \n", + " Calculates the loss.\n", + " \n", + " Args:\n", + " experiences: (tuple) tuple of [\"state\", \"action\", \"reward\", \"next_state\", \"done\"] namedtuples\n", + " gamma: (float) The discount factor.\n", + " q_network: (tf.keras.Sequential) Keras model for predicting the q_values\n", + " target_q_network: (tf.keras.Sequential) Karas model for predicting the targets\n", + " \n", + " Returns:\n", + " loss: (TensorFlow Tensor(shape=(0,), dtype=int32)) the Mean-Squared Error between\n", + " the y targets and the Q(s,a) values.\n", + " \"\"\"\n", + "\n", + " \n", + " # Unpack the mini-batch of experience tuples\n", + " states, actions, rewards, next_states, done_vals = experiences\n", + " \n", + " # Compute max Q^(s,a)\n", + " max_qsa = tf.reduce_max(target_q_network(next_states), axis=-1)\n", + " \n", + " # Set y = R if episode terminates, otherwise set y = R + γ max Q^(s,a).\n", + " y_targets = rewards + (gamma * max_qsa * (1 - done_vals))\n", + " \n", + " # Get the q_values\n", + " q_values = q_network(states)\n", + " q_values = tf.gather_nd(q_values, tf.stack([tf.range(q_values.shape[0]),\n", + " tf.cast(actions, tf.int32)], axis=1))\n", + " \n", + " # Calculate the loss\n", + " loss = MSE(y_targets, q_values)\n", + " \n", + " return loss\n", + "\n", + "``` \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 8 - Update the Network Weights\n", + "\n", + "We will use the `agent_learn` function below to implement lines ***12 -14*** of the algorithm outlined in [Fig 3](#7). The `agent_learn` function will update the weights of the $Q$ and target $\\hat Q$ networks using a custom training loop. Because we are using a custom training loop we need to retrieve the gradients via a `tf.GradientTape` instance, and then call `optimizer.apply_gradients()` to update the weights of our $Q$-Network. Note that we are also using the `@tf.function` decorator to increase performance. Without this decorator our training will take twice as long. If you would like to know more about how to increase performance with `@tf.function` take a look at the [TensorFlow documentation](https://www.tensorflow.org/guide/function).\n", + "\n", + "The last line of this function updates the weights of the target $\\hat Q$-Network using a [soft update](#6.1). If you want to know how this is implemented in code we encourage you to take a look at the `utils.update_target_network` function in the `utils` module." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@tf.function\n", + "def agent_learn(experiences, gamma):\n", + " \"\"\"\n", + " Updates the weights of the Q networks.\n", + " \n", + " Args:\n", + " experiences: (tuple) tuple of [\"state\", \"action\", \"reward\", \"next_state\", \"done\"] namedtuples\n", + " gamma: (float) The discount factor.\n", + " \n", + " \"\"\"\n", + " \n", + " # Calculate the loss\n", + " with tf.GradientTape() as tape:\n", + " loss = compute_loss(experiences, gamma, q_network, target_q_network)\n", + "\n", + " # Get the gradients of the loss with respect to the weights.\n", + " gradients = tape.gradient(loss, q_network.trainable_variables)\n", + " \n", + " # Update the weights of the q_network.\n", + " optimizer.apply_gradients(zip(gradients, q_network.trainable_variables))\n", + "\n", + " # update the weights of target q_network\n", + " utils.update_target_network(q_network, target_q_network)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 9 - Train the Agent\n", + "\n", + "We are now ready to train our agent to solve the Lunar Lander environment. In the cell below we will implement the algorithm in [Fig 3](#7) line by line (please note that we have included the same algorithm below for easy reference. This will prevent you from scrolling up and down the notebook):\n", + "\n", + "* **Line 1**: We initialize the `memory_buffer` with a capacity of $N =$ `MEMORY_SIZE`. Notice that we are using a `deque` as the data structure for our `memory_buffer`.\n", + "\n", + "\n", + "* **Line 2**: We skip this line since we already initialized the `q_network` in [Exercise 1](#ex01).\n", + "\n", + "\n", + "* **Line 3**: We initialize the `target_q_network` by setting its weights to be equal to those of the `q_network`.\n", + "\n", + "\n", + "* **Line 4**: We start the outer loop. Notice that we have set $M =$ `num_episodes = 2000`. This number is reasonable because the agent should be able to solve the Lunar Lander environment in less than `2000` episodes using this notebook's default parameters.\n", + "\n", + "\n", + "* **Line 5**: We use the `.reset()` method to reset the environment to the initial state and get the initial state.\n", + "\n", + "\n", + "* **Line 6**: We start the inner loop. Notice that we have set $T =$ `max_num_timesteps = 1000`. This means that the episode will automatically terminate if the episode hasn't terminated after `1000` time steps.\n", + "\n", + "\n", + "* **Line 7**: The agent observes the current `state` and chooses an `action` using an $\\epsilon$-greedy policy. Our agent starts out using a value of $\\epsilon =$ `epsilon = 1` which yields an $\\epsilon$-greedy policy that is equivalent to the equiprobable random policy. This means that at the beginning of our training, the agent is just going to take random actions regardless of the observed `state`. As training progresses we will decrease the value of $\\epsilon$ slowly towards a minimum value using a given $\\epsilon$-decay rate. We want this minimum value to be close to zero because a value of $\\epsilon = 0$ will yield an $\\epsilon$-greedy policy that is equivalent to the greedy policy. This means that towards the end of training, the agent will lean towards selecting the `action` that it believes (based on its past experiences) will maximize $Q(s,a)$. We will set the minimum $\\epsilon$ value to be `0.01` and not exactly 0 because we always want to keep a little bit of exploration during training. If you want to know how this is implemented in code we encourage you to take a look at the `utils.get_action` function in the `utils` module.\n", + "\n", + "\n", + "* **Line 8**: We use the `.step()` method to take the given `action` in the environment and get the `reward` and the `next_state`. \n", + "\n", + "\n", + "* **Line 9**: We store the `experience(state, action, reward, next_state, done)` tuple in our `memory_buffer`. Notice that we also store the `done` variable so that we can keep track of when an episode terminates. This allowed us to set the $y$ targets in [Exercise 2](#ex02).\n", + "\n", + "\n", + "* **Line 10**: We check if the conditions are met to perform a learning update. We do this by using our custom `utils.check_update_conditions` function. This function checks if $C =$ `NUM_STEPS_FOR_UPDATE = 4` time steps have occured and if our `memory_buffer` has enough experience tuples to fill a mini-batch. For example, if the mini-batch size is `64`, then our `memory_buffer` should have at least `64` experience tuples in order to pass the latter condition. If the conditions are met, then the `utils.check_update_conditions` function will return a value of `True`, otherwise it will return a value of `False`.\n", + "\n", + "\n", + "* **Lines 11 - 14**: If the `update` variable is `True` then we perform a learning update. The learning update consists of sampling a random mini-batch of experience tuples from our `memory_buffer`, setting the $y$ targets, performing gradient descent, and updating the weights of the networks. We will use the `agent_learn` function we defined in [Section 8](#8) to perform the latter 3.\n", + "\n", + "\n", + "* **Line 15**: At the end of each iteration of the inner loop we set `next_state` as our new `state` so that the loop can start again from this new state. In addition, we check if the episode has reached a terminal state (i.e we check if `done = True`). If a terminal state has been reached, then we break out of the inner loop.\n", + "\n", + "\n", + "* **Line 16**: At the end of each iteration of the outer loop we update the value of $\\epsilon$, and check if the environment has been solved. We consider that the environment has been solved if the agent receives an average of `200` points in the last `100` episodes. If the environment has not been solved we continue the outer loop and start a new episode.\n", + "\n", + "Finally, we wanted to note that we have included some extra variables to keep track of the total number of points the agent received in each episode. This will help us determine if the agent has solved the environment and it will also allow us to see how our agent performed during training. We also use the `time` module to measure how long the training takes. \n", + "\n", + "
\n", + "
\n", + "
\n", + " \n", + "
Fig 4. Deep Q-Learning with Experience Replay.
\n", + "
\n", + "
\n", + "\n", + "**Note:** With this notebook's default parameters, the following cell takes between 10 to 15 minutes to run. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start = time.time()\n", + "\n", + "num_episodes = 2000\n", + "max_num_timesteps = 1000\n", + "\n", + "total_point_history = []\n", + "\n", + "num_p_av = 100 # number of total points to use for averaging\n", + "epsilon = 1.0 # initial ε value for ε-greedy policy\n", + "\n", + "# Create a memory buffer D with capacity N\n", + "memory_buffer = deque(maxlen=MEMORY_SIZE)\n", + "\n", + "# Set the target network weights equal to the Q-Network weights\n", + "target_q_network.set_weights(q_network.get_weights())\n", + "\n", + "for i in range(num_episodes):\n", + " \n", + " # Reset the environment to the initial state and get the initial state\n", + " state = env.reset()\n", + " total_points = 0\n", + " \n", + " for t in range(max_num_timesteps):\n", + " \n", + " # From the current state S choose an action A using an ε-greedy policy\n", + " state_qn = np.expand_dims(state, axis=0) # state needs to be the right shape for the q_network\n", + " q_values = q_network(state_qn)\n", + " action = utils.get_action(q_values, epsilon)\n", + " \n", + " # Take action A and receive reward R and the next state S'\n", + " next_state, reward, done, _ = env.step(action)\n", + " \n", + " # Store experience tuple (S,A,R,S') in the memory buffer.\n", + " # We store the done variable as well for convenience.\n", + " memory_buffer.append(experience(state, action, reward, next_state, done))\n", + " \n", + " # Only update the network every NUM_STEPS_FOR_UPDATE time steps.\n", + " update = utils.check_update_conditions(t, NUM_STEPS_FOR_UPDATE, memory_buffer)\n", + " \n", + " if update:\n", + " # Sample random mini-batch of experience tuples (S,A,R,S') from D\n", + " experiences = utils.get_experiences(memory_buffer)\n", + " \n", + " # Set the y targets, perform a gradient descent step,\n", + " # and update the network weights.\n", + " agent_learn(experiences, GAMMA)\n", + " \n", + " state = next_state.copy()\n", + " total_points += reward\n", + " \n", + " if done:\n", + " break\n", + " \n", + " total_point_history.append(total_points)\n", + " av_latest_points = np.mean(total_point_history[-num_p_av:])\n", + " \n", + " # Update the ε value\n", + " epsilon = utils.get_new_eps(epsilon)\n", + "\n", + " print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\", end=\"\")\n", + "\n", + " if (i+1) % num_p_av == 0:\n", + " print(f\"\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\")\n", + "\n", + " # We will consider that the environment is solved if we get an\n", + " # average of 200 points in the last 100 episodes.\n", + " if av_latest_points >= 200.0:\n", + " print(f\"\\n\\nEnvironment solved in {i+1} episodes!\")\n", + " q_network.save('lunar_lander_model.h5')\n", + " break\n", + " \n", + "tot_time = time.time() - start\n", + "\n", + "print(f\"\\nTotal Runtime: {tot_time:.2f} s ({(tot_time/60):.2f} min)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the point history to see how our agent improved during training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E_EUXxurfe8m", + "scrolled": false + }, + "outputs": [], + "source": [ + "# Plot the point history\n", + "utils.plot_history(total_point_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c_xwgaX5MnYt" + }, + "source": [ + "\n", + "## 10 - See the Trained Agent In Action\n", + "\n", + "Now that we have trained our agent, we can see it in action. We will use the `utils.create_video` function to create a video of our agent interacting with the environment using the trained $Q$-Network. The `utils.create_video` function uses the `imageio` library to create the video. This library produces some warnings that can be distracting, so, to suppress these warnings we run the code below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Suppress warnings from imageio\n", + "import logging\n", + "logging.getLogger().setLevel(logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below we create a video of our agent interacting with the Lunar Lander environment using the trained `q_network`. The video is saved to the `videos` folder with the given `filename`. We use the `utils.embed_mp4` function to embed the video in the Jupyter Notebook so that we can see it here directly without having to download it.\n", + "\n", + "We should note that since the lunar lander starts with a random initial force applied to its center of mass, every time you run the cell below you will see a different video. If the agent was trained properly, it should be able to land the lunar lander in the landing pad every time, regardless of the initial force applied to its center of mass." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3Ttb_zLeJKiG" + }, + "outputs": [], + "source": [ + "filename = \"./videos/lunar_lander.mp4\"\n", + "\n", + "utils.create_video(filename, env, q_network)\n", + "utils.embed_mp4(filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 11 - Congratulations!\n", + "\n", + "You have successfully used Deep Q-Learning with Experience Replay to train an agent to land a lunar lander safely on a landing pad on the surface of the moon. Congratulations!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12 - References\n", + "\n", + "If you would like to learn more about Deep Q-Learning, we recommend you check out the following papers.\n", + "\n", + "\n", + "* [Human-level Control Through Deep Reinforcement Learning](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)\n", + "\n", + "\n", + "* [Continuous Control with Deep Reinforcement Learning](https://arxiv.org/pdf/1509.02971.pdf)\n", + "\n", + "\n", + "* [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "TensorFlow - Lunar Lander.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/__pycache__/public_tests.cpython-37.pyc b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/__pycache__/public_tests.cpython-37.pyc new file mode 100644 index 00000000..e02e79a2 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/__pycache__/public_tests.cpython-37.pyc differ diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/__pycache__/utils.cpython-37.pyc b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/__pycache__/utils.cpython-37.pyc new file mode 100644 index 00000000..454d4eb2 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/__pycache__/utils.cpython-37.pyc differ diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/deep_q_algorithm.png b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/deep_q_algorithm.png new file mode 100644 index 00000000..8ccdf346 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/deep_q_algorithm.png differ diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/lunar_lander.gif b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/lunar_lander.gif new file mode 100644 index 00000000..feda4ec2 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/lunar_lander.gif differ diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/rl_formalism.png b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/rl_formalism.png new file mode 100644 index 00000000..d3d44328 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/images/rl_formalism.png differ diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/public_tests.py b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/public_tests.py new file mode 100644 index 00000000..6e057758 --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/public_tests.py @@ -0,0 +1,79 @@ +from tensorflow.keras.activations import relu, linear +from tensorflow.keras.layers import Dense +from tensorflow.keras.optimizers import Adam + +import numpy as np + +def test_network(target): + num_actions = 4 + state_size = 8 + i = 0 + assert len(target.layers) == 3, f"Wrong number of layers. Expected 3 but got {len(target.layers)}" + assert target.input.shape.as_list() == [None, state_size], \ + f"Wrong input shape. Expected [None, 400] but got {target.input.shape.as_list()}" + expected = [[Dense, [None, 64], relu], + [Dense, [None, 64], relu], + [Dense, [None, num_actions], linear]] + + for layer in target.layers: + assert type(layer) == expected[i][0], \ + f"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}" + assert layer.output.shape.as_list() == expected[i][1], \ + f"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}" + assert layer.activation == expected[i][2], \ + f"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}" + i = i + 1 + + print("\033[92mAll tests passed!") + +def test_optimizer(target, ALPHA): + assert type(target) == Adam, f"Wrong optimizer. Expected: {Adam}, got: {target}" + assert np.isclose(target.learning_rate.numpy(), ALPHA), f"Wrong alpha. Expected: {ALPHA}, got: {target.learning_rate.numpy()}" + print("\033[92mAll tests passed!") + + +def test_compute_loss(target): + num_actions = 4 + def target_q_network_random(inputs): + return np.float32(np.random.rand(inputs.shape[0],num_actions)) + + def q_network_random(inputs): + return np.float32(np.random.rand(inputs.shape[0],num_actions)) + + def target_q_network_ones(inputs): + return np.float32(np.ones((inputs.shape[0], num_actions))) + + def q_network_ones(inputs): + return np.float32(np.ones((inputs.shape[0], num_actions))) + + np.random.seed(1) + states = np.float32(np.random.rand(64, 8)) + actions = np.float32(np.floor(np.random.uniform(0, 1, (64, )) * 4)) + rewards = np.float32(np.random.rand(64, )) + next_states = np.float32(np.random.rand(64, 8)) + done_vals = np.float32((np.random.uniform(0, 1, size=(64,)) > 0.96) * 1) + + loss = target((states, actions, rewards, next_states, done_vals), 0.995, q_network_random, target_q_network_random) + + + assert np.isclose(loss, 0.6991737), f"Wrong value. Expected {0.6991737}, got {loss}" + + # Test when episode terminates + done_vals = np.float32(np.ones((64,))) + loss = target((states, actions, rewards, next_states, done_vals), 0.995, q_network_ones, target_q_network_ones) + assert np.isclose(loss, 0.343270182), f"Wrong value. Expected {0.343270182}, got {loss}" + + # Test MSE with parameters A = B + done_vals = np.float32((np.random.uniform(0, 1, size=(64,)) > 0.96) * 1) + rewards = np.float32(np.ones((64, ))) + loss = target((states, actions, rewards, next_states, done_vals), 0, q_network_ones, target_q_network_ones) + assert np.isclose(loss, 0), f"Wrong value. Expected {0}, got {loss}" + + # Test MSE with parameters A = 0 and B = 1 + done_vals = np.float32((np.random.uniform(0, 1, size=(64,)) > 0.96) * 1) + rewards = np.float32(np.zeros((64, ))) + loss = target((states, actions, rewards, next_states, done_vals), 0, q_network_ones, target_q_network_ones) + assert np.isclose(loss, 1), f"Wrong value. Expected {1}, got {loss}" + + print("\033[92mAll tests passed!") + \ No newline at end of file diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/utils.py b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/utils.py new file mode 100644 index 00000000..91f0ec7a --- /dev/null +++ b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/utils.py @@ -0,0 +1,139 @@ +import base64 +import random +from itertools import zip_longest + +import imageio +import IPython +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import numpy as np +import pandas as pd +import tensorflow as tf +from statsmodels.iolib.table import SimpleTable + + +SEED = 0 # seed for pseudo-random number generator +MINIBATCH_SIZE = 64 # mini-batch size +TAU = 1e-3 # soft update parameter +E_DECAY = 0.995 # ε decay rate for ε-greedy policy +E_MIN = 0.01 # minimum ε value for ε-greedy policy + + +random.seed(SEED) + + +def get_experiences(memory_buffer): + experiences = random.sample(memory_buffer, k=MINIBATCH_SIZE) + states = tf.convert_to_tensor(np.array([e.state for e in experiences if e is not None]),dtype=tf.float32) + actions = tf.convert_to_tensor(np.array([e.action for e in experiences if e is not None]), dtype=tf.float32) + rewards = tf.convert_to_tensor(np.array([e.reward for e in experiences if e is not None]), dtype=tf.float32) + next_states = tf.convert_to_tensor(np.array([e.next_state for e in experiences if e is not None]),dtype=tf.float32) + done_vals = tf.convert_to_tensor(np.array([e.done for e in experiences if e is not None]).astype(np.uint8), + dtype=tf.float32) + return (states, actions, rewards, next_states, done_vals) + + +def check_update_conditions(t, num_steps_upd, memory_buffer): + if (t + 1) % num_steps_upd == 0 and len(memory_buffer) > MINIBATCH_SIZE: + return True + else: + return False + + +def get_new_eps(epsilon): + return max(E_MIN, E_DECAY*epsilon) + + +def get_action(q_values, epsilon=0): + if random.random() > epsilon: + return np.argmax(q_values.numpy()[0]) + else: + return random.choice(np.arange(4)) + + +def update_target_network(q_network, target_q_network): + for target_weights, q_net_weights in zip(target_q_network.weights, q_network.weights): + target_weights.assign(TAU * q_net_weights + (1.0 - TAU) * target_weights) + + +def plot_history(reward_history, rolling_window=20, lower_limit=None, + upper_limit=None, plot_rw=True, plot_rm=True): + + if lower_limit is None or upper_limit is None: + rh = reward_history + xs = [x for x in range(len(reward_history))] + else: + rh = reward_history[lower_limit:upper_limit] + xs = [x for x in range(lower_limit,upper_limit)] + + df = pd.DataFrame(rh) + rollingMean = df.rolling(rolling_window).mean() + + plt.figure(figsize=(10,7), facecolor='white') + + if plot_rw: + plt.plot(xs, rh, linewidth=1, color='cyan') + if plot_rm: + plt.plot(xs, rollingMean, linewidth=2, color='magenta') + + text_color = 'black' + + ax = plt.gca() + ax.set_facecolor('black') + plt.grid() +# plt.title("Total Point History", color=text_color, fontsize=40) + plt.xlabel('Episode', color=text_color, fontsize=30) + plt.ylabel('Total Points', color=text_color, fontsize=30) + yNumFmt = mticker.StrMethodFormatter('{x:,}') + ax.yaxis.set_major_formatter(yNumFmt) + ax.tick_params(axis='x', colors=text_color) + ax.tick_params(axis='y', colors=text_color) + plt.show() + + +def display_table(initial_state, action, next_state, reward, done): + + action_labels = ["Do nothing", "Fire right engine", "Fire main engine", "Fire left engine"] + + # Do not use column headers + column_headers = None + + with np.printoptions(formatter={'float': '{:.3f}'.format}): + table_info = [("Initial State:", [f"{initial_state}"]), + ("Action:", [f"{action_labels[action]}"]), + ("Next State:", [f"{next_state}"]), + ("Reward Received:", [f"{reward:.3f}"]), + ("Episode Terminated:", [f"{done}"])] + + # Generate table + row_labels, data = zip_longest(*table_info) + table = SimpleTable(data, column_headers, row_labels) + + return table + + +def embed_mp4(filename): + """Embeds an mp4 file in the notebook.""" + video = open(filename,'rb').read() + b64 = base64.b64encode(video) + tag = ''' + '''.format(b64.decode()) + return IPython.display.HTML(tag) + + +def create_video(filename, env, q_network, fps=30): + with imageio.get_writer(filename, fps=fps) as video: + done = False + state = env.reset() + frame = env.render(mode="rgb_array") + video.append_data(frame) + while not done: + state = np.expand_dims(state, axis=0) + q_values = q_network(state) + action = np.argmax(q_values.numpy()[0]) + state, _, done, _ = env.step(action) + frame = env.render(mode="rgb_array") + video.append_data(frame) \ No newline at end of file diff --git a/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/videos/rl_formalism.m4v b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/videos/rl_formalism.m4v new file mode 100644 index 00000000..2fa8aac7 Binary files /dev/null and b/Unsupervised learning recommenders reinforcement learning/week3/5 Practice Lab-Reinforcement Learning/videos/rl_formalism.m4v differ