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#@title Copyright 2020 Google LLC. Double-click here for license information.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
After doing this Colab, you'll know how to do the following:
The dataset for this exercise is based on 1990 census data from California. The dataset is old but still provides a great opportunity to learn about machine learning programming.
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#@title Run on TensorFlow 2.x
%tensorflow_version 2.x
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#@title Import relevant modules
import pandas as pd
import tensorflow as tf
from matplotlib import pyplot as plt
# The following lines adjust the granularity of reporting.
pd.options.display.max_rows = 10
pd.options.display.float_format = "{:.1f}".format
Datasets are often stored on disk or at a URL in .csv format.
A well-formed .csv file contains column names in the first row, followed by many rows of data. A comma divides each value in each row. For example, here are the first five rows of the .csv file file holding the California Housing Dataset:
"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"
-114.310000,34.190000,15.000000,5612.000000,1283.000000,1015.000000,472.000000,1.493600,66900.000000
-114.470000,34.400000,19.000000,7650.000000,1901.000000,1129.000000,463.000000,1.820000,80100.000000
-114.560000,33.690000,17.000000,720.000000,174.000000,333.000000,117.000000,1.650900,85700.000000
-114.570000,33.640000,14.000000,1501.000000,337.000000,515.000000,226.000000,3.191700,73400.000000This Colab, like many machine learning programs, gathers the .csv file and stores the data in memory as a pandas Dataframe. pandas is an open source Python library. The primary datatype in pandas is a DataFrame. You can imagine a pandas DataFrame as a spreadsheet in which each row is identified by a number and each column by a name. pandas is itself built on another open source Python library called NumPy. If you aren't familiar with these technologies, please view these two quick tutorials:
The following code cell imports the .csv file into a pandas DataFrame and scales the values in the label (median_house_value):
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# Import the dataset.
training_df = pd.read_csv(filepath_or_buffer="https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv")
# Scale the label.
training_df["median_house_value"] /= 1000.0
# Print the first rows of the pandas DataFrame.
training_df.head()
Scaling median_house_value puts the value of each house in units of thousands. Scaling will keep loss values and learning rates in a friendlier range.
Although scaling a label is usually not essential, scaling features in a multi-feature model usually is essential.
A large part of most machine learning projects is getting to know your data. The pandas API provides a describe function that outputs the following statistics about every column in the DataFrame:
count, which is the number of rows in that column. Ideally, count contains the same value for every column.
mean and std, which contain the mean and standard deviation of the values in each column.
min and max, which contain the lowest and highest values in each column.
25%, 50%, 75%, which contain various quantiles.
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# Get statistics on the dataset.
training_df.describe()
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#@title Double-click to view a possible answer.
# The maximum value (max) of several columns seems very
# high compared to the other quantiles. For example,
# example the total_rooms column. Given the quantile
# values (25%, 50%, and 75%), you might expect the
# max value of total_rooms to be approximately
# 5,000 or possibly 10,000. However, the max value
# is actually 37,937.
# When you see anomalies in a column, become more careful
# about using that column as a feature. That said,
# anomalies in potential features sometimes mirror
# anomalies in the label, which could make the column
# be (or seem to be) a powerful feature.
# Also, as you will see later in the course, you
# might be able to represent (pre-process) raw data
# in order to make columns into useful features.
The following code defines two functions:
build_model(my_learning_rate), which builds a randomly-initialized model.train_model(model, feature, label, epochs), which trains the model from the examples (feature and label) you pass. Since you don't need to understand model building code right now, we've hidden this code cell. You may optionally double-click the following headline to see the code that builds and trains a model.
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#@title Define the functions that build and train a model
def build_model(my_learning_rate):
"""Create and compile a simple linear regression model."""
# Most simple tf.keras models are sequential.
model = tf.keras.models.Sequential()
# Describe the topography of the model.
# The topography of a simple linear regression model
# is a single node in a single layer.
model.add(tf.keras.layers.Dense(units=1,
input_shape=(1,)))
# Compile the model topography into code that TensorFlow can efficiently
# execute. Configure training to minimize the model's mean squared error.
model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),
loss="mean_squared_error",
metrics=[tf.keras.metrics.RootMeanSquaredError()])
return model
def train_model(model, df, feature, label, epochs, batch_size):
"""Train the model by feeding it data."""
# Feed the model the feature and the label.
# The model will train for the specified number of epochs.
history = model.fit(x=df[feature],
y=df[label],
batch_size=batch_size,
epochs=epochs)
# Gather the trained model's weight and bias.
trained_weight = model.get_weights()[0]
trained_bias = model.get_weights()[1]
# The list of epochs is stored separately from the rest of history.
epochs = history.epoch
# Isolate the error for each epoch.
hist = pd.DataFrame(history.history)
# To track the progression of training, we're going to take a snapshot
# of the model's root mean squared error at each epoch.
rmse = hist["root_mean_squared_error"]
return trained_weight, trained_bias, epochs, rmse
print("Defined the create_model and traing_model functions.")
The following matplotlib functions create the following plots:
You may optionally double-click the headline to see the matplotlib code, but note that writing matplotlib code is not an important part of learning ML programming.
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#@title Define the plotting functions
def plot_the_model(trained_weight, trained_bias, feature, label):
"""Plot the trained model against 200 random training examples."""
# Label the axes.
plt.xlabel(feature)
plt.ylabel(label)
# Create a scatter plot from 200 random points of the dataset.
random_examples = training_df.sample(n=200)
plt.scatter(random_examples[feature], random_examples[label])
# Create a red line representing the model. The red line starts
# at coordinates (x0, y0) and ends at coordinates (x1, y1).
x0 = 0
y0 = trained_bias
x1 = 10000
y1 = trained_bias + (trained_weight * x1)
plt.plot([x0, x1], [y0, y1], c='r')
# Render the scatter plot and the red line.
plt.show()
def plot_the_loss_curve(epochs, rmse):
"""Plot a curve of loss vs. epoch."""
plt.figure()
plt.xlabel("Epoch")
plt.ylabel("Root Mean Squared Error")
plt.plot(epochs, rmse, label="Loss")
plt.legend()
plt.ylim([rmse.min()*0.97, rmse.max()])
plt.show()
print("Defined the plot_the_model and plot_the_loss_curve functions.")
An important part of machine learning is determining which features correlate with the label. For example, real-life home-value prediction models typically rely on hundreds of features and synthetic features. However, this model relies on only one feature. For now, you'll arbitrarily use total_rooms as that feature.
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# The following variables are the hyperparameters.
learning_rate = 0.01
epochs = 30
batch_size = 30
# Specify the feature and the label.
my_feature = "total_rooms" # the total number of rooms on a specific city block.
my_label="median_house_value" # the median value of a house on a specific city block.
# That is, you're going to create a model that predicts house value based
# solely on total_rooms.
# Discard any pre-existing version of the model.
my_model = None
# Invoke the functions.
my_model = build_model(learning_rate)
weight, bias, epochs, rmse = train_model(my_model, training_df,
my_feature, my_label,
epochs, batch_size)
print("\nThe learned weight for your model is %.4f" % weight)
print("The learned bias for your model is %.4f\n" % bias )
plot_the_model(weight, bias, my_feature, my_label)
plot_the_loss_curve(epochs, rmse)
A certain amount of randomness plays into training a model. Consequently, you'll get different results each time you train the model. That said, given the dataset and the hyperparameters, the trained model will generally do a poor job describing the feature's relation to the label.
You can use the trained model to make predictions. In practice, you should make predictions on examples that are not used in training. However, for this exercise, you'll just work with a subset of the same training dataset. A later Colab exercise will explore ways to make predictions on examples not used in training.
First, run the following code to define the house prediction function:
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def predict_house_values(n, feature, label):
"""Predict house values based on a feature."""
batch = training_df[feature][10000:10000 + n]
predicted_values = my_model.predict_on_batch(x=batch)
print("feature label predicted")
print(" value value value")
print(" in thousand$ in thousand$")
print("--------------------------------------")
for i in range(n):
print ("%5.0f %6.0f %15.0f" % (training_df[feature][10000 + i],
training_df[label][10000 + i],
predicted_values[i][0] ))
Now, invoke the house prediction function on 10 examples:
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predict_house_values(10, my_feature, my_label)
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#@title Double-click to view the answer.
# Most of the predicted values differ significantly
# from the label value, so the trained model probably
# doesn't have much predictive power. However, the
# first 10 examples might not be representative of
# the rest of the examples.
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my_feature = "?" # Replace the ? with population or possibly
# a different column name.
# Experiment with the hyperparameters.
learning_rate = 2
epochs = 3
batch_size = 120
# Don't change anything below this line.
my_model = build_model(learning_rate)
weight, bias, epochs, rmse = train_model(my_model, training_df,
my_feature, my_label,
epochs, batch_size)
plot_the_model(weight, bias, my_feature, my_label)
plot_the_loss_curve(epochs, rmse)
predict_house_values(15, my_feature, my_label)
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#@title Double-click to view a possible solution.
my_feature = "population" # Pick a feature other than "total_rooms"
# Possibly, experiment with the hyperparameters.
learning_rate = 0.05
epochs = 18
batch_size = 3
# Don't change anything below.
my_model = build_model(learning_rate)
weight, bias, epochs, rmse = train_model(my_model, training_df,
my_feature, my_label,
epochs, batch_size)
plot_the_model(weight, bias, my_feature, my_label)
plot_the_loss_curve(epochs, rmse)
predict_house_values(10, my_feature, my_label)
Did population produce better predictions than total_rooms?
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#@title Double-click to view the answer.
# Training is not entirely deterministic, but population
# typically converges at a slightly higher RMSE than
# total_rooms. So, population appears to be about
# the same or slightly worse at making predictions
# than total_rooms.
You have determined that total_rooms and population were not useful features. That is, neither the total number of rooms in a neighborhood nor the neighborhood's population successfully predicted the median house price of that neighborhood. Perhaps though, the ratio of total_rooms to population might have some predictive power. That is, perhaps block density relates to median house value.
To explore this hypothesis, do the following:
total_rooms to population. (If you are new to pandas DataFrames, please study the .)
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# Define a synthetic feature named rooms_per_person
training_df["rooms_per_person"] = ? # write your code here.
# Don't change the next line.
my_feature = "rooms_per_person"
# Assign values to these three hyperparameters.
learning_rate = ?
epochs = ?
batch_size = ?
# Don't change anything below this line.
my_model = build_model(learning_rate)
weight, bias, epochs, rmse = train_model(my_model, training_df,
my_feature, my_label,
epochs, batch_size)
plot_the_loss_curve(epochs, rmse)
predict_house_values(15, my_feature, my_label)
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#@title Double-click to view a possible solution to Task 4.
# Define a synthetic feature
training_df["rooms_per_person"] = training_df["total_rooms"] / training_df["population"]
my_feature = "rooms_per_person"
# Tune the hyperparameters.
learning_rate = 0.06
epochs = 24
batch_size = 30
# Don't change anything below this line.
my_model = build_model(learning_rate)
weight, bias, epochs, mae = train_model(my_model, training_df,
my_feature, my_label,
epochs, batch_size)
plot_the_loss_curve(epochs, mae)
predict_house_values(15, my_feature, my_label)
Based on the loss values, this synthetic feature produces a better model than the individual features you tried in Task 2 and Task 3. However, the model still isn't creating great predictions.
So far, we've relied on trial-and-error to identify possible features for the model. Let's rely on statistics instead.
A correlation matrix indicates how each attribute's raw values relate to the other attributes' raw values. Correlation values have the following meanings:
1.0: perfect positive correlation; that is, when one attribute rises, the other attribute rises.-1.0: perfect negative correlation; that is, when one attribute rises, the other attribute falls. 0.0: no correlation; the two column's are not linearly related.In general, the higher the absolute value of a correlation value, the greater its predictive power. For example, a correlation value of -0.8 implies far more predictive power than a correlation of -0.2.
The following code cell generates the correlation matrix for attributes of the California Housing Dataset:
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# Generate a correlation matrix.
training_df.corr()
The correlation matrix shows nine potential features (including a synthetic
feature) and one label (median_house_value). A strong negative correlation or strong positive correlation with the label suggests a potentially good feature.
Your Task: Determine which of the nine potential features appears to be the best candidate for a feature?
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#@title Double-click here for the solution to Task 5
# The `median_income` correlates 0.7 with the label
# (median_house_value), so median_income` might be a
# good feature. The other seven potential features
# all have a correlation relatively close to 0.
# If time permits, try median_income as the feature
# and see whether the model improves.
Correlation matrices don't tell the entire story. In later exercises, you'll find additional ways to unlock predictive power from potential features.
Note: Using median_income as a feature may raise some ethical and fairness
issues. Towards the end of the course, we'll explore ethical and fairness issues.