From 9e08f9b8d94071e8edc933f13678ea041667fd4e Mon Sep 17 00:00:00 2001 From: nidhal baccouri Date: Thu, 8 Oct 2020 22:05:04 +0200 Subject: [PATCH 1/5] started hyperparam search --- examples/hyperparams-search/fit.py | 24 ++++++++++++++++++++++++ examples/hyperparams-search/igel.yaml | 27 +++++++++++++++++++++++++++ igel/hyperparams.py | 6 ++++++ igel/igel.py | 4 +++- 4 files changed, 60 insertions(+), 1 deletion(-) create mode 100644 examples/hyperparams-search/fit.py create mode 100644 examples/hyperparams-search/igel.yaml create mode 100644 igel/hyperparams.py diff --git a/examples/hyperparams-search/fit.py b/examples/hyperparams-search/fit.py new file mode 100644 index 0000000..f4acf46 --- /dev/null +++ b/examples/hyperparams-search/fit.py @@ -0,0 +1,24 @@ +from igel import Igel + +""" +The goal of igel is to use ML without writing code. Therefore, the right and simplest way to use igel is from terminal. +You can run ` igel fit -dp path_to_dataset -yml path_to_yaml_file`. + +Alternatively, you can write code if you want. This example below demonstrates how to use igel if you want to write code. +However, I suggest you try and use the igel CLI. Type igel -h in your terminal to know more. + +=============================================================================================================== + +This example fits a machine learning model on the indian-diabetes dataset + +- default model here is the neural network and the configuration are provided in neural-network.yaml file +- You can switch to random forest by providing the random-forest.yaml as the config file in the parameters + +""" + +mock_fit_params = {'data_path': '../data/indian-diabetes/train-indians-diabetes.csv', + 'yaml_path': './igel.yaml', + 'cmd': 'fit'} + +Igel(**mock_fit_params) + diff --git a/examples/hyperparams-search/igel.yaml b/examples/hyperparams-search/igel.yaml new file mode 100644 index 0000000..784eac0 --- /dev/null +++ b/examples/hyperparams-search/igel.yaml @@ -0,0 +1,27 @@ +# dataset operations +dataset: + type: csv + split: # split options + test_size: 0.2 # 0.2 means 20% for the test data, so 80% are automatically for training + shuffle: True # whether to shuffle the data before/while splitting + + preprocess: + scale: # scaling options + method: standard # standardization will scale values to have a 0 mean and 1 standard deviation | you can also try minmax + target: inputs # scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled + + +# model definition +model: + type: classification + algorithm: RandomForest + hyperparameter_search: + max_depth: + - 6 + - 10 + n_estimators: [100, 300] + max_features: [auto, sqrt] + +# target you want to predict +target: + - sick diff --git a/igel/hyperparams.py b/igel/hyperparams.py new file mode 100644 index 0000000..09e97d4 --- /dev/null +++ b/igel/hyperparams.py @@ -0,0 +1,6 @@ +from sklearn.model_selection import GridSearchCV, RandomizedSearchCV + + +def hyperparameter_search(method, **kwargs): + search = GridSearchCV(**kwargs) if method == 'grid_search' else RandomizedSearchCV(**kwargs) + return search.best_estimator_, search.best_score_, search.best_params_ diff --git a/igel/igel.py b/igel/igel.py index dd240c0..b18b20c 100644 --- a/igel/igel.py +++ b/igel/igel.py @@ -338,7 +338,9 @@ def fit(self, **kwargs): logger.info("performing cross validation ...") cv_results = cross_validate(estimator=self.model, X=x_train, - y=y_train, **cv_params) + y=y_train, + **cv_params) + self.model.fit(x_train, y_train) else: # if the model type is clustering From 3dcaf50de2b8c73a8db3ed3df21a21eca1e4fe7f Mon Sep 17 00:00:00 2001 From: nidhal baccouri Date: Fri, 9 Oct 2020 22:42:02 +0200 Subject: [PATCH 2/5] implemented hyperparm search --- examples/hyperparams-search/igel.yaml | 15 ++-- .../model_results/description.json | 76 ++++++++++++++++++ .../model_results/model.sav | Bin 0 -> 1696475 bytes igel/hyperparams.py | 14 +++- igel/igel.py | 24 +++++- 5 files changed, 121 insertions(+), 8 deletions(-) create mode 100644 examples/hyperparams-search/model_results/description.json create mode 100644 examples/hyperparams-search/model_results/model.sav diff --git a/examples/hyperparams-search/igel.yaml b/examples/hyperparams-search/igel.yaml index 784eac0..ea1f2cc 100644 --- a/examples/hyperparams-search/igel.yaml +++ b/examples/hyperparams-search/igel.yaml @@ -16,11 +16,16 @@ model: type: classification algorithm: RandomForest hyperparameter_search: - max_depth: - - 6 - - 10 - n_estimators: [100, 300] - max_features: [auto, sqrt] + method: grid_search + parameter_grid: + max_depth: [6, 10] + n_estimators: [100, 300] + max_features: [auto, sqrt] + arguments: + cv: 5 + refit: true + return_train_score: false + verbose: 0 # target you want to predict target: diff --git a/examples/hyperparams-search/model_results/description.json b/examples/hyperparams-search/model_results/description.json new file mode 100644 index 0000000..cb4a7e2 --- /dev/null +++ b/examples/hyperparams-search/model_results/description.json @@ -0,0 +1,76 @@ +{ + "model": "RandomForestClassifier", + "arguments": "default", + "type": "classification", + "algorithm": "RandomForest", + "dataset_props": { + "type": "csv", + "split": { + "test_size": 0.2, + "shuffle": true + }, + "preprocess": { + "scale": { + "method": "standard", + "target": "inputs" + } + } + }, + "model_props": { + "type": "classification", + "algorithm": "RandomForest", + "hyperparameter_search": { + "method": "grid_search", + "parameter_grid": { + "max_depth": [ + 6, + 10 + ], + "n_estimators": [ + 100, + 300 + ], + "max_features": [ + "auto", + "sqrt" + ] + }, + "arguments": { + "cv": 5, + "refit": true, + "return_train_score": false, + "verbose": 0 + } + } + }, + "data_path": "../data/indian-diabetes/train-indians-diabetes.csv", + "train_data_shape": [ + 614, + 8 + ], + "test_data_shape": [ + 154, + 8 + ], + "train_data_size": 614, + "test_data_size": 154, + "results_path": "/home/nidhal/projects/igel/examples/hyperparams-search/model_results", + "model_path": 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**kwargs): "results_path": str(self.results_path), "model_path": str(self.default_model_path), "target": None if self.model_type == 'clustering' else self.target, - "results_on_test_data": eval_results + "results_on_test_data": eval_results, + "hyperparameter_search_results": hp_search_results } if self.model_type == 'clustering': From f6f10fc76a8b9f76c9e469040ad0e54d6a7a3487 Mon Sep 17 00:00:00 2001 From: nidhal baccouri Date: Fri, 9 Oct 2020 23:08:17 +0200 Subject: [PATCH 3/5] updated history --- HISTORY.rst | 9 +++ .../model_results/description.json | 76 ------------------ .../model_results/model.sav | Bin 1696475 -> 0 bytes igel/__init__.py | 2 +- setup.cfg | 2 +- 5 files changed, 11 insertions(+), 78 deletions(-) delete mode 100644 examples/hyperparams-search/model_results/description.json delete mode 100644 examples/hyperparams-search/model_results/model.sav diff --git a/HISTORY.rst b/HISTORY.rst index f655b40..b1f451d 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,15 @@ History ======= + + +0.2.8 (2020-10-09) +------------------- + +* implemented hyperparameter search +* added examples + + 0.2.7 (2020-10-05) ------------------- diff --git a/examples/hyperparams-search/model_results/description.json b/examples/hyperparams-search/model_results/description.json deleted file mode 100644 index cb4a7e2..0000000 --- a/examples/hyperparams-search/model_results/description.json +++ /dev/null @@ -1,76 +0,0 @@ -{ - "model": "RandomForestClassifier", - "arguments": "default", - "type": "classification", - "algorithm": "RandomForest", - "dataset_props": { - "type": "csv", - "split": { - "test_size": 0.2, - "shuffle": true - }, - "preprocess": { - "scale": { - "method": "standard", - "target": "inputs" - } - } - }, - "model_props": { - "type": "classification", - "algorithm": "RandomForest", - "hyperparameter_search": { - "method": "grid_search", - "parameter_grid": { - "max_depth": [ - 6, - 10 - ], - "n_estimators": [ - 100, - 300 - ], - "max_features": [ - "auto", - "sqrt" - ] - }, - "arguments": { - "cv": 5, - "refit": true, - "return_train_score": false, - "verbose": 0 - } - } - }, - "data_path": "../data/indian-diabetes/train-indians-diabetes.csv", - "train_data_shape": [ - 614, - 8 - ], - "test_data_shape": [ - 154, - 8 - ], - "train_data_size": 614, - "test_data_size": 154, - "results_path": "/home/nidhal/projects/igel/examples/hyperparams-search/model_results", - "model_path": "/home/nidhal/projects/igel/examples/hyperparams-search/model_results/model.sav", - "target": [ - "sick" - ], - "results_on_test_data": { - "accuracy_score": 0.7662337662337663, - "f1_score": 0.6249999999999999, - "precision_score": 0.7317073170731707, - "recall_score": 0.5454545454545454 - }, - "hyperparameter_search_results": { - "best_params": 0.7687858190057311, - "best_score": { - "max_depth": 6, - "max_features": "auto", - "n_estimators": 300 - } - } -} \ No newline at end of file diff --git a/examples/hyperparams-search/model_results/model.sav 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hyperparameter_search: - method: grid_search + method: random_search parameter_grid: max_depth: [6, 10] n_estimators: [100, 300] diff --git a/igel/hyperparams.py b/igel/hyperparams.py index be077d5..ba0c79f 100644 --- a/igel/hyperparams.py +++ b/igel/hyperparams.py @@ -7,10 +7,19 @@ def hyperparameter_search(model, x_train, y_train, **kwargs): - search = GridSearchCV(model, - params, - **kwargs) if method == 'grid_search' else RandomizedSearchCV(model, - params, - **kwargs) + + search = None + if method == 'grid_search': + search = GridSearchCV(model, + params, + **kwargs) + + elif method == 'random_search': + search = RandomizedSearchCV(model, + params, + **kwargs) + else: + raise Exception("hyperparameter method must be grid_search or random_search") + search.fit(x_train, y_train) return search.best_estimator_, search.best_score_, search.best_params_ From f9e8a7b15c19d26309e10c1b0fa143d52cb5f1bb Mon Sep 17 00:00:00 2001 From: nidhal baccouri Date: Fri, 9 Oct 2020 23:41:51 +0200 Subject: [PATCH 5/5] updatd readme --- README.rst | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/README.rst b/README.rst index ad4a23a..6d6c8bb 100644 --- a/README.rst +++ b/README.rst @@ -76,6 +76,7 @@ Features - Supports different data preprocessing methods - Provides flexibility and data control while writing configurations - Supports cross validation +- Supports both hyperparameter search (version >= 0.2.8) - Supports yaml and json format - Supports different sklearn metrics for regression, classification and clustering - Supports multi-output/multi-target regression and classification @@ -90,6 +91,7 @@ a **single line of code** All you need is a **yaml** (or **json**) file, where you need to describe what you are trying to do. That's it! Igel supports all sklearn's machine learning functionality, whether regression, classification or clustering. +Precisely, you can use **63** different machine learning model in igel. Installation ------------- @@ -434,6 +436,16 @@ Here is an overview of all supported configurations (for now): cv: # [int] -> number of kfold (default 5) n_jobs: # [signed int] -> The number of CPUs to use to do the computation (default None) verbose: # [int] -> The verbosity level. (default 0) + hyperparameter_search: + method: grid_search # method you want to use: grid_search and random_search are supported + parameter_grid: # put your parameters grid here that you want to use, an example is provided below + param1: [val1, val2] + param2: [val1, val2] + arguments: # additional arguments you want to provide for the hyperparameter search + cv: 5 # number of folds + refit: true # whether to refit the model after the search + return_train_score: false # whether to return the train score + verbose: 0 # verbosity level # target you want to predict target: # list of strings: basically put here the column(s), you want to predict that exist in your csv dataset @@ -709,6 +721,8 @@ is conducted on the target column to show you more the capabilities of igel. Furthermore, the multioutput-example contains a **multioutput regression** example. Finally, the cv-example contains an example using the Ridge classifier using cross validation. +You can also find a cross validation and a hyperparameter search examples in the folder. + I suggest you play around with the examples and igel cli. However, you can also directly execute the fit.py, evaluate.py and predict.py if you want to.