{{ message }}

Instantly share code, notes, and snippets.

# Michel Kana michelkana

Created Aug 31, 2021
 best_nb_iterations = np.array([ab_test_scores[:,i].argmax() for i in range(len(tree_depths)) ]) best_test_scores = np.array([ab_test_scores[best_nb_iterations[i],i] for i in range(len(tree_depths)) ]) optimal_tree_depth_idx = best_test_scores.argmax() optimal_nb_iterations = best_nb_iterations[optimal_tree_depth_idx] optimal_tree_depth = tree_depths[optimal_tree_depth_idx] optimal_test_score = ab_test_scores[optimal_nb_iterations, optimal_tree_depth_idx] optimal_train_score = ab_train_scores[optimal_nb_iterations, optimal_tree_depth_idx] print('The combination of base learner depth {} and {} iterations achieves the best accuracy {}% on test set \ and {}% on training set.'.format(optimal_tree_depth, optimal_nb_iterations, round(optimal_test_score*100,5), round(optimal_train_score*100,5)))
Created Aug 31, 2021
 from sklearn.ensemble import AdaBoostClassifier # function to run boosting with gradient descent def run_adaboosting(X_train, y_train, X_test, y_test, depths=[3], iterations=800, lr=0.05): fig, ax = plt.subplots(1,2,figsize=(20,5)) ab_train_scores = np.zeros((iterations, len(depths))) ab_test_scores = np.zeros((iterations, len(depths))) for i, depth in enumerate(depths): ab_model = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=depth), n_estimators=iterations, learning_rate=lr) ab_model.fit(X_train, y_train);
Created Aug 31, 2021
View boosting_simplified.py
 # Single decision tree1 trained on original dataset tree1 = DecisionTreeClassifier(max_depth=3).fit(X_train, y_train) y_train_predicted_tree1 = tree1.predict(X_train) y_test_predicted_tree1 = tree1.predict(X_test) # Modified dataset, weighted by residuals y_train_predicted_tree1_bool = y_train_predicted_tree1 == y_train sample_weights = np.ones(len(X_train)) sample_weights[np.logical_not(y_train_predicted_tree1_bool)] = 2
Created Aug 31, 2021
View boosting_samples_distribution.py
 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.tree import DecisionTreeClassifier # Load data data_train = pd.read_csv('data/Higgs_train.csv') data_test = pd.read_csv('data/Higgs_test.csv')
Created Jun 30, 2021
View mle_products_aic.py
 # akaike information criterion aic_A = 2*5 - 2*math.log(ml_A) aic_B = 2*5 - 2*math.log(ml_B) print("AIC for Product A: {:.2f}".format(aic_A)) print("AIC for Product B: {:.2f}".format(aic_B))
Created Jun 19, 2021
View truecasing_bleu.py
 with open("path/to/yelp_academic_dataset_review_small.json") as f: reviews = f.readlines() orig_reviews = [json.loads(r)['text'].replace('\n','') for r in reviews] lowercase_reviews = [r.lower() for r in orig_reviews] truecase_reviews = [truecasing(r) for r in lowercase_reviews] bleu_scores = [get_bleu([ro], [rt]) for ro, rt in zip(orig_reviews, truecase_reviews)] round(sum(bleu_scores)/len(bleu_scores), 2)
Created Jun 19, 2021
View truecasing_pos.py
 # packages needed # !pip install nltk # !pip install stanfordnlp # !pip install --upgrade bleu import nltk from nltk.tokenize import sent_tokenize import re import stanfordnlp from bleu import list_bleu
Created May 31, 2021
View saliency_map.py
 # pip install scipy==1.1.0 from vis.visualization import visualize_saliency def plot_saliency(img_idx=None): img_idx = plot_features_map(img_idx) grads = visualize_saliency(cnn_saliency, -1, filter_indices=ytest[img_idx][0], seed_input=x_test[img_idx], backprop_modifier=None, grad_modifier="absolute") predicted_label = labels[np.argmax(cnn.predict(x_test[img_idx].reshape(1,32,32,3)),1)[0]] fig, ax = plt.subplots(1,2, figsize=(10,5))
Created May 31, 2021
View saliency_map_features.py
 from random import randint import matplotlib.pylab as plt import numpy as np def get_feature_maps(model, layer_id, input_image): model_ = Model(inputs=[model.input], outputs=[model.layers[layer_id].output]) return model_.predict(np.expand_dims(input_image, axis=0))[0,:,:,:].transpose((2,0,1))
Created May 31, 2021
View saliency_maps_training.py
 from keras.datasets import cifar10 from keras.utils import np_utils from keras.models import Sequential, Model from keras.layers import Dense, Dropout, Flatten from keras.layers.convolutional import Conv2D, MaxPooling2D from keras import regularizers from keras.layers import BatchNormalization from keras.optimizers import RMSprop from keras.preprocessing.image import ImageDataGenerator