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hw_1_doc_claas_fig_gen.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
mydict = {'earn': 2866, 'acq': 1632, 'money-fx': 475, 'grain': 371, 'crude': 330, 'trade': 319, 'interest': 219, 'ship': 137, 'money-supply': 133, 'sugar': 106, 'coffee': 99, 'gold': 87, 'gnp': 80, 'veg-oil': 60, 'cpi': 60, 'oilseed': 50, 'cocoa': 48, 'reserves': 42, 'livestock': 42, 'jobs': 39, 'bop': 38, 'copper': 37, 'ipi': 37, 'rubber': 32, 'alum': 31, 'iron-steel': 31, 'nat-gas': 27, 'carcass': 20, 'retail': 20, 'dlr': 20, 'wheat': 19, 'gas': 19, 'tin': 18, 'pet-chem': 18, 'cotton': 17, 'housing': 16, 'meal-feed': 15, 'wpi': 15, 'orange': 13, 'lei': 11, 'zinc': 10, 'strategic-metal': 9, 'hog': 9, 'heat': 8, 'lumber': 8, 'corn': 8, 'silver': 7, 'income': 7, 'instal-debt': 5, 'lead': 5, 'soybean': 4, 'tea': 4, 'fuel': 4, 'nickel': 3, 'inventories': 3, 'cpu': 3, 'potato': 2, 'l-cattle': 2, 'jet': 2, 'palm-oil': 2, 'rapeseed': 2, 'stg': 2, 'plywood': 2, 'propane': 1, 'fishmeal': 1, 'tapioca': 1, 'rice': 1, 'rand': 1, 'platinum': 1, 'saudriyal': 1, 'nzdlr': 1, 'coconut': 1, 'groundnut': 1, 'wool': 1, 'austdlr': 1, 'soy-meal': 1, 'barley': 1, 'cruzado': 1}
key_list = []
val_list = []
for key, value in mydict.items():
# print("key ", key, "value ", value)
key_list.append(key)
val_list.append(value)
print( key, ":", value)
print("key_list = ", key_list)
print("val_list = ", val_list)
plt.ylabel('Number of Documents')
plt.xlabel("Document Label")
plt.plot(val_list, color='green', marker='x', linestyle='dashed', linewidth=2, markersize=12)
plt.show()
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
new_dic_for_10_class = {'earn': 2866, 'acq': 1632, 'money-fx': 475, 'grain': 371, 'crude': 330, 'trade': 319, 'interest': 219, 'ship': 137, 'money-supply': 133, 'sugar': 106}
objects = ('earn', 'acq', 'money-fx', 'grain', 'crude', 'trade', 'interest', 'ship', 'money-supply','sugar')
y_pos = np.arange(len(objects))
performance = [2866,1632,475,371,330,319, 219 , 137, 133, 106]
plt.bar(y_pos, performance, align='center', color='green',alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('Number of Documents')
plt.xlabel('Topic Name')
plt.show()
true_list = ['earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 'earn', 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from sklearn.metrics import accuracy_score, precision_score, recall_score
print( 'Accuracy score: ', accuracy_score(true_label_list, pred_label_list))
top_10_class_list = ['earn', 'acq', 'money-fx', 'grain', 'crude', 'trade', 'interest', 'ship', 'money-supply', 'sugar']
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
cm = confusion_matrix(true_label_list, pred_label_list)
print("cm ", cm)
sns.heatmap(cm, square=True, annot=True, cmap='GnBu', cbar=False,
xticklabels=[top_10_class_list], yticklabels=[top_10_class_list])
plt.xlabel('true label')
plt.ylabel('predicted label')
plt.show()