text stringlengths 0 4.99k |
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print(row.title, \":\", row.genres) |
print(\"----\" * 8) |
print(\"Top 10 movie recommendations\") |
print(\"----\" * 8) |
recommended_movies = movie_df[movie_df[\"movieId\"].isin(recommended_movie_ids)] |
for row in recommended_movies.itertuples(): |
print(row.title, \":\", row.genres) |
Showing recommendations for user: 474 |
==================================== |
Movies with high ratings from user |
-------------------------------- |
Fugitive, The (1993) : Thriller |
Remains of the Day, The (1993) : Drama|Romance |
West Side Story (1961) : Drama|Musical|Romance |
X2: X-Men United (2003) : Action|Adventure|Sci-Fi|Thriller |
Spider-Man 2 (2004) : Action|Adventure|Sci-Fi|IMAX |
-------------------------------- |
Top 10 movie recommendations |
-------------------------------- |
Dazed and Confused (1993) : Comedy |
Ghost in the Shell (Kôkaku kidôtai) (1995) : Animation|Sci-Fi |
Drugstore Cowboy (1989) : Crime|Drama |
Road Warrior, The (Mad Max 2) (1981) : Action|Adventure|Sci-Fi|Thriller |
Dark Knight, The (2008) : Action|Crime|Drama|IMAX |
Inglourious Basterds (2009) : Action|Drama|War |
Up (2009) : Adventure|Animation|Children|Drama |
Dark Knight Rises, The (2012) : Action|Adventure|Crime|IMAX |
Star Wars: Episode VII - The Force Awakens (2015) : Action|Adventure|Fantasy|Sci-Fi|IMAX |
Thor: Ragnarok (2017) : Action|Adventure|Sci-Fi |
Demonstration of how to handle highly imbalanced classification problems. |
Introduction |
This example looks at the Kaggle Credit Card Fraud Detection dataset to demonstrate how to train a classification model on data with highly imbalanced classes. |
First, vectorize the CSV data |
import csv |
import numpy as np |
# Get the real data from https://www.kaggle.com/mlg-ulb/creditcardfraud/ |
fname = \"/Users/fchollet/Downloads/creditcard.csv\" |
all_features = [] |
all_targets = [] |
with open(fname) as f: |
for i, line in enumerate(f): |
if i == 0: |
print(\"HEADER:\", line.strip()) |
continue # Skip header |
fields = line.strip().split(\",\") |
all_features.append([float(v.replace('\"', \"\")) for v in fields[:-1]]) |
all_targets.append([int(fields[-1].replace('\"', \"\"))]) |
if i == 1: |
print(\"EXAMPLE FEATURES:\", all_features[-1]) |
features = np.array(all_features, dtype=\"float32\") |
targets = np.array(all_targets, dtype=\"uint8\") |
print(\"features.shape:\", features.shape) |
print(\"targets.shape:\", targets.shape) |
HEADER: \"Time\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\",\"V20\",\"V21\",\"V22\",\"V23\",\"V24\",\"V25\",\"V26\",\"V27\",\"V28\",\"Amount\",\"Class\" |
EXAMPLE FEATURES: [0.0, -1.3598071336738, -0.0727811733098497, 2.53634673796914, 1.37815522427443, -0.338320769942518, 0.462387777762292, 0.239598554061257, 0.0986979012610507, 0.363786969611213, 0.0907941719789316, -0.551599533260813, -0.617800855762348, -0.991389847235408, -0.311169353699879, 1.46817697209427, -0.470400525259478, 0.207971241929242, 0.0257905801985591, 0.403992960255733, 0.251412098239705, -0.018306777944153, 0.277837575558899, -0.110473910188767, 0.0669280749146731, 0.128539358273528, -0.189114843888824, 0.133558376740387, -0.0210530534538215, 149.62] |
features.shape: (284807, 30) |
targets.shape: (284807, 1) |
Prepare a validation set |
num_val_samples = int(len(features) * 0.2) |
train_features = features[:-num_val_samples] |
train_targets = targets[:-num_val_samples] |
val_features = features[-num_val_samples:] |
val_targets = targets[-num_val_samples:] |
print(\"Number of training samples:\", len(train_features)) |
print(\"Number of validation samples:\", len(val_features)) |
Number of training samples: 227846 |
Number of validation samples: 56961 |
Analyze class imbalance in the targets |
counts = np.bincount(train_targets[:, 0]) |
print( |
\"Number of positive samples in training data: {} ({:.2f}% of total)\".format( |
counts[1], 100 * float(counts[1]) / len(train_targets) |
) |
) |
weight_for_0 = 1.0 / counts[0] |
weight_for_1 = 1.0 / counts[1] |
Number of positive samples in training data: 417 (0.18% of total) |
Normalize the data using training set statistics |
mean = np.mean(train_features, axis=0) |
train_features -= mean |
val_features -= mean |
std = np.std(train_features, axis=0) |
train_features /= std |
val_features /= std |
Build a binary classification model |
from tensorflow import keras |
model = keras.Sequential( |
[ |
keras.layers.Dense( |
256, activation=\"relu\", input_shape=(train_features.shape[-1],) |
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