Adding titanic example that uses all features

Author: ilblackdragon

titanic_all_features.py

@@ -0,0 +1,84 @@
+import random
+import pandas
+from sklearn.cross_validation import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+from sklearn.utils import check_array
+
+import tensorflow as tf
+from tensorflow.contrib import layers
+from tensorflow.contrib import learn
+
+
+train = pandas.read_csv('data/titanic_train.csv')
+y = train.pop('Survived')
+# Drop all unique columns. List all variables for future reference.
+categorical_vars = ['Pclass', 'Sex', 'Embarked']
+continues_vars = ['Age', 'SibSp', 'Parch', 'Fare']
+X = train[categorical_vars + continues_vars].fillna(0)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+
+# Pandas input functino. 
+def pandas_input_fn(x, y=None, batch_size=128, num_epochs=None):
+  def input_fn():
+    if y is not None:
+      x['target'] = y
+    queue = learn.dataframe.queues.feeding_functions.enqueue_data(
+      x, 1000, shuffle=num_epochs is None, num_epochs=num_epochs)
+    if num_epochs is None:
+      features = queue.dequeue_many(batch_size)
+    else:
+      features = queue.dequeue_up_to(batch_size)
+    features = dict(zip(['index'] + list(x.columns), features))
+    if y is not None:
+      target = features.pop('target')
+      return features, target
+    return features
+  return input_fn
+
+
+# Process categorical variables into ids.
+X_train = X_train.copy()
+X_test = X_test.copy()
+categorical_var_encoders = {}
+for var in categorical_vars:
+  le = LabelEncoder().fit(X_train[var])
+  X_train[var + '_ids'] = le.transform(X_train[var])
+  X_test[var + '_ids'] = le.transform(X_test[var])
+  X_train.pop(var)
+  X_test.pop(var)
+  categorical_var_encoders[var] = le
+
+
+CATEGORICAL_EMBED_SIZE = 10 # Note, you can customize this per variable.
+
+
+# 3 layer neural network with hyperbolic tangent activation.
+def dnn_tanh(features, target):
+    target = tf.one_hot(target, 2, 1.0, 0.0)
+    # Organize continues features.
+    final_features = [tf.expand_dims(tf.cast(features[var], tf.float32), 1) for var in continues_vars]
+    # Embed categorical variables into distributed representation.
+    for var in categorical_vars:
+        feature = learn.ops.categorical_variable(
+            features[var + '_ids'], len(categorical_var_encoders[var].classes_), 
+            embedding_size=CATEGORICAL_EMBED_SIZE, name=var)
+        final_features.append(feature)
+    # Concatenate all features into one vector.
+    features = tf.concat(1, final_features)
+    # Deep Neural Network
+    logits = layers.stack(features, layers.fully_connected, [10, 20, 10],
+        activation_fn=tf.tanh)
+    prediction, loss = learn.models.logistic_regression(logits, target)
+    train_op = layers.optimize_loss(loss,
+        tf.contrib.framework.get_global_step(), optimizer='SGD', learning_rate=0.05)
+    return tf.argmax(prediction, dimension=1), loss, train_op
+
+random.seed(42)
+classifier = learn.Estimator(model_fn=dnn_tanh)
+# Note: not training this alomst at all.
+classifier.fit(input_fn=pandas_input_fn(X_train, y_train), steps=100)
+preds = list(classifier.predict(input_fn=pandas_input_fn(X_test, num_epochs=1), as_iterable=True))
+print(accuracy_score(y_test, preds))

titanic_all_features_with_fc.py

@@ -0,0 +1,115 @@
+import random
+import pandas
+from sklearn.cross_validation import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+from sklearn.utils import check_array
+
+import tensorflow as tf
+from tensorflow.contrib import layers
+from tensorflow.contrib import learn
+
+
+train = pandas.read_csv('data/titanic_train.csv')
+y = train.pop('Survived')
+# Drop all unique columns. List all variables for future reference.
+categorical_vars = ['Pclass', 'Sex', 'Embarked']
+continues_vars = ['Age', 'SibSp', 'Parch', 'Fare']
+X = train[categorical_vars + continues_vars].fillna(0)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+
+# Pandas input functino. 
+def pandas_input_fn(x, y=None, batch_size=128, num_epochs=None):
+  def input_fn():
+    if y is not None:
+      x['target'] = y
+    queue = learn.dataframe.queues.feeding_functions.enqueue_data(
+      x, 1000, shuffle=num_epochs is None, num_epochs=num_epochs)
+    if num_epochs is None:
+      features = queue.dequeue_many(batch_size)
+    else:
+      features = queue.dequeue_up_to(batch_size)
+    features = dict(zip(['index'] + list(x.columns), features))
+    if y is not None:
+      target = features.pop('target')
+      return features, target
+    return features
+  return input_fn
+
+
+# Process categorical variables into ids.
+X_train = X_train.copy()
+X_test = X_test.copy()
+categorical_var_encoders = {}
+for var in categorical_vars:
+  le = LabelEncoder().fit(X_train[var])
+  X_train[var + '_ids'] = le.transform(X_train[var])
+  X_test[var + '_ids'] = le.transform(X_test[var])
+  X_train.pop(var)
+  X_test.pop(var)
+  categorical_var_encoders[var] = le
+
+### Note: Feature Columns currently (2016/10/22) not working, update is coming.
+# Setup feature columns.
+CATEGORICAL_EMBED_SIZE = 10 # Note, you can customize this per variable.
+feature_columns = [
+  layers.real_valued_column(var) for var in continues_vars
+] + [
+  layers.embedding_column(
+     layers.sparse_column_with_integerized_feature(
+       var + '_ids', len(categorical_var_encoders[var].classes_)), 
+     CATEGORICAL_EMBED_SIZE) for var in
+  categorical_vars
+]
+
+
+# Linear classifier.
+'''
+random.seed(42)
+tflr = learn.LinearClassifier(n_classes=2,
+    feature_columns=feature_columns,
+    optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
+tflr.fit(input_fn=train_input_fn, steps=500)
+print(list(tflr.predict(input_fn=test_input_fn, as_iterable=True)), y_test)
+print(accuracy_score(y_test, list(tflr.predict(input_fn=test_input_fn, as_iterable=True))))
+'''
+
+# 3 layer neural network with rectified linear activation.
+'''
+random.seed(42)
+classifier = learn.DNNClassifier(hidden_units=[10, 20, 10],
+    n_classes=2,
+    feature_columns=feature_columns,
+    optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
+classifier.fit(X_train, y_train, batch_size=128, steps=500)
+print(accuracy_score(y_test, classifier.predict(X_test)))
+'''
+
+# 3 layer neural network with hyperbolic tangent activation.
+def dnn_tanh(features, target):
+    target = tf.one_hot(target, 2, 1.0, 0.0)
+    # Organize continues features.
+    final_features = [tf.expand_dims(tf.cast(features[var], tf.float32), 1) for var in continues_vars]
+    # Embed categorical variables into distributed representation.
+    for var in categorical_vars:
+        feature = learn.ops.categorical_variable(
+            features[var + '_ids'], len(categorical_var_encoders[var].classes_), 
+            embedding_size=CATEGORICAL_EMBED_SIZE, name=var)
+        final_features.append(feature)
+    # Concatenate all features into one vector.
+    features = tf.concat(1, final_features)
+    # Deep Neural Network
+    logits = layers.stack(features, layers.fully_connected, [10, 20, 10],
+        activation_fn=tf.tanh)
+    prediction, loss = learn.models.logistic_regression(logits, target)
+    train_op = layers.optimize_loss(loss,
+        tf.contrib.framework.get_global_step(), optimizer='SGD', learning_rate=0.05)
+    return tf.argmax(prediction, dimension=1), loss, train_op
+
+random.seed(42)
+classifier = learn.Estimator(model_fn=dnn_tanh)
+classifier.fit(input_fn=pandas_input_fn(X_train, y_train), steps=100)
+preds = list(classifier.predict(input_fn=pandas_input_fn(X_test, num_epochs=1), as_iterable=True))
+print(accuracy_score(y_test, preds))