In this tutorial, you will learn how to check the data and prepare it to create a linear regression task.
This tutorial is divided into two parts:
- Look for interaction
- Test the model
In the previous tutorial, you used the Boston dataset to estimate the median price of a house. Boston dataset has a small size, with only 506 observations. This dataset is considered as a benchmark to try new linear regression algorithms.
The dataset is composed of:
| Variable | Description |
| zn | The proportion of residential land zoned for lots over 25,000 sq.ft. |
| indus | The proportion of non-retail business acres per town. |
| nox | nitric oxides concentration |
| rm | average number of rooms per dwelling |
| age | the proportion of owner-occupied units built before 1940 |
| dis | weighted distances to five Boston employment centers |
| tax | full-value property-tax rate per dollars 10,000 |
| ptratio | the pupil-teacher ratio by a town |
| medv | The median value of owner-occupied homes in thousand dollars |
| crim | per capita crime rate by town |
| chas | Charles River dummy variable (1 if bounds river; 0 otherwise) |
| B | the proportion of blacks by the town |
In this tutorial, we will estimate the median price using a linear regressor, but the focus is on one particular process of machine learning: "data preparation."
A model generalizes the pattern in the data. To capture such a pattern, you need to find it first. A good practice is to perform a data analysis before running any machine learning algorithm.
Choosing the right features makes all the difference in the success of your model. Imagine you try to estimate the wage of a people, if you do not include the gender as a covariate, you end up with a poor estimate.
Another way to improve the model is to look at the correlation between the independent variable. Back to the example, you can think of education as an excellent candidate to predict the wage but also the occupation. It is fair to say, the occupation depends on the level of education, namely higher education often leads to a better occupation. If we generalize this idea, we can say the correlation between the dependent variable and an explanatory variable can be magnified of yet another explanatory variable.
To capture the limited effect of education on occupation, we can use an interaction term.
If you look at the wage equation, it becomes:
If 
is positive, then it implies that an additional level of education yields a higher increase in the median value of a house for a high occupation level. In other words, there is an interaction effect between education and occupation.
In this tutorial, we will try to see which variables can be a good candidate for interaction terms. We will test if adding this kind of information leads to better price prediction.
Summary statistics
There are a few steps you can follow before proceeding to the model. As mentioned earlier, the model is a generalization of the data. The best practice is to understand the data and the make a prediction. If you do not know your data, you have slim chances to improve your model.
As a first step, load the data as a pandas dataframe and create a training set and testing set.
Tips: For this tutorial, you need to have matplotlit and seaborn installed in Python. You can install Python package on the fly with Jupyter. You Should not do this
!conda install -- yes matplotlib
but
import sys
!{sys.executable} -m pip install matplotlib # Already installed
!{sys.executable} -m pip install seaborn
Note that this step is not necessary if you have matplotlib and seaborn installed.
Matplotlib is the library to create a graph in Python. Seaborn is a statistical visualization library built on top of matplotlib. It provides attractive and beautiful plots.
The code below imports the necessary libraries.
import pandas as pd from sklearn import datasets import tensorflow as tf from sklearn.datasets import load_boston import numpy as np
The library sklearn includes the Boston dataset. You can call its API to import the data.
boston = load_boston() df = pd.DataFrame(boston.data)
The feature's name are stored in the object feature_names in an array.
boston.feature_names
Output
array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD','TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')
You can rename the columns.
df.columns = boston.feature_names df['PRICE'] = boston.target df.head(2)
You convert the variable CHAS as a string variable and label it with yes if CHAS = 1 and no if CHAS = 0
df['CHAS'] = df['CHAS'].map({1:'yes', 0:'no'})
df['CHAS'].head(5)
0 no
1 no
2 no
3 no
4 no
Name: CHAS, dtype: object
With pandas, it is straightforward to split the dataset. You randomly divide the dataset with 80 percent training set and 20 percent testing set. Pandas have a built-in function to split a data frame sample.
The first parameter frac is a value from 0 to 1. You set it to 0.8 to select randomly 80 percent of the data frame.
Random_state allows to have the same dataframe returned for everyone.
### Create train/test set df_train=df.sample(frac=0.8,random_state=200) df_test=df.drop(df_train.index)
You can get the shape of the data. It should be:
- Train set: 506*0.8 = 405
- Test set: 506*0.2 = 101
print(df_train.shape, df_test.shape)
Output
(405, 14) (101, 14)
df_test.head(5)
Output
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | PRICE | |
| 0 | 0.00632 | 18.0 | 2.31 | no | 0.538 | 6.575 | 65.2 | 4.0900 | 1.0 | 296.0 | 15.3 | 396.90 | 4.98 | 24.0 |
| 1 | 0.02731 | 0.0 | 7.07 | no | 0.469 | 6.421 | 78.9 | 4.9671 | 2.0 | 242.0 | 17.8 | 396.90 | 9.14 | 21.6 |
| 3 | 0.03237 | 0.0 | 2.18 | no | 0.458 | 6.998 | 45.8 | 6.0622 | 3.0 | 222.0 | 18.7 | 394.63 | 2.94 | 33.4 |
| 6 | 0.08829 | 12.5 | 7.87 | no | 0.524 | 6.012 | 66.6 | 5.5605 | 5.0 | 311.0 | 15.2 | 395.60 | 12.43 | 22.9 |
| 7 | 0.14455 | 12.5 | 7.87 | no | 0.524 | 6.172 | 96.1 | 5.9505 | 5.0 | 311.0 | 15.2 | 396.90 | 19.15 | 27.1 |
Data is messy; it's often misbalanced and sprinkled with outlier values that throw off the analysis and machine learning training.
The first step to getting the dataset cleaned up is understanding where it needs cleaning. Cleaning up a dataset can be tricky to do, especially in any generalizable manner
Google Research team has developed a tool for this job called Facets that help to visualize the data and slice it in all sorts of manners. This is a good starting point to comprehend how the dataset is laid out.
Facets allow you to find where the data does not quite look the way you are thinking.
Except for their web app, Google makes it easy to embed the toolkit into a Jupyter notebook.
There are two parts to Facets:
- Facets Overview
- Facets Deep Dive
Facets Overview
Facets Overview gives an overview of the dataset. Facets Overview splits the columns of the data into rows of salient information showing
- the percentage of missing observation
- min and max values
- statistics like the mean, median, and standard deviation.
- It also adds a column that shows the percentage of values that are zeroes, which is helpful when most of the values are zeroes.
- It is possible to see these distributions on the test dataset as well as the training set for each feature. It means you can double-check that the test has a similar distribution to the training dataset.
This is at least the minimum to do before any machine learning task. With this tool, you do not miss this crucial step, and it highlights some abnormalities.
Facets Deep Dive
Facets Deep Dive is a cool tool. It allowsto have some clarity on your dataset and zoom all the way in to see an individual piece of data. It means you can facet the data by row and column across any of the features of the dataset.
We will use these two tools with the Boston dataset.
Note: You cannot use Facets Overview and Facets Deep Dive at the same time. You need to clear the notebook first to change the tool.
Install Facet
You can use the Facet web app for most of the analysis. In this tutorial, you will see how to use it within a Jupyter Notebook.
First of all, you need to install nbextensions. It is done with this code. You copy and paste the following code in the terminal of your machine.
pip install jupyter_contrib_nbextensions
Right after that, you need to clone the repositories in your computer. You have two choices:
Option 1) Copy and paste this code in the terminal (Recommended)
If you do not have Git installed on your machine, please go to this URL https://git-scm.com/download/win and follow the instruction. Once you are done, you can use the git command in the terminal for Mac User or Anaconda prompt for Windows user
git clone https://github.com/PAIR-code/facets
Option 2) Go to https://github.com/PAIR-code/facets and download the repositories.
If you choose the first option, the file ends up in your download file. You can either let the file in download or drag it to another path.
You can check where Facets is stored with this command line:
echo `pwd`/`ls facets`
Now that you have located Facets, you need to install it in Jupyter Notebook. You need to set the working directory to the path where facets is located.
Your present working directory and location of Facets zip should be same.
You need to point the working directory to Facet:
cd facets
To install Facets in Jupyter, you have two options. If you installed Jupyter with Conda for all the users, copy this code:
can use jupyter nbextension install facets-dist/
jupyter nbextension install facets-dist/
Otherwise, use:
jupyter nbextension install facets-dist/ --user
All right, you are all set. Let's open Facet Overview.
Overview
Overview uses a Python script to compute the statistics. You need to import the script called generic_feature_statistics_generator to Jupyter. Don't worry; the script is located in the facets files.
You need to locate its path. It is easily done. You open facets, open the file facets_overview and then python. Copy the path
After that, go back to Jupyter, and write the following code. Change the path '/Users/Thomas/facets/facets_overview/python' to your path.
# Add the facets overview python code to the python path# Add t
import sys
sys.path.append('/Users/Thomas/facets/facets_overview/python')
You can import the script with the code below.
from generic_feature_statistics_generator import GenericFeatureStatisticsGenerator
In windows, the same code becomes
import sys sys.path.append(r"C:\Users\Admin\Anaconda3\facets-master\facets_overview\python") from generic_feature_statistics_generator import GenericFeatureStatisticsGenerator
To calculate the feature statistics, you need to use the function GenericFeatureStatisticsGenerator(), and you use the object ProtoFromDataFrames. You can pass the data frame in a dictionary. For instance, if we want to create a summary statistic for the train set, we can store the information in a dictionary and use it in the object `ProtoFromDataFrames``
-
'name': 'train', 'table': df_train
Name is the name of the table displays, and you use the name of the table you want to compute the summary. In your example, the table containing the data is df_train
# Calculate the feature statistics proto from the datasets and stringify it for use in facets overview
import base64
gfsg = GenericFeatureStatisticsGenerator()
proto = gfsg.ProtoFromDataFrames([{'name': 'train', 'table': df_train},
{'name': 'test', 'table': df_test}])
#proto = gfsg.ProtoFromDataFrames([{'name': 'train', 'table': df_train}])
protostr = base64.b64encode(proto.SerializeToString()).decode("utf-8")
Lastly, you just copy and paste the code below. The code comes directly from GitHub. You should be able to see this:
# Display the facets overview visualization for this data# Displ
from IPython.core.display import display, HTML
HTML_TEMPLATE = """<link rel="import" href="/nbextensions/facets-dist/facets-jupyter.html" >
<facets-overview id="elem"></facets-overview>
<script>
document.querySelector("#elem").protoInput = "{protostr}";
</script>"""
html = HTML_TEMPLATE.format(protostr=protostr)
display(HTML(html))
Graph
After you check the data and their distribution, you can plot a correlation matrix. The correlation matrix computes the Pearson coefficient. This coefficient is bonded between -1 and 1, with a positive value indicates a positive correlation and negative value a negative correlation.
You are interested to see which variables can be a good candidate for interaction terms.
## Choose important feature and further check with Dive
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="ticks")
# Compute the correlation matrix
corr = df.corr('pearson')
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,annot=True,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
Output
<matplotlib.axes._subplots.AxesSubplot at 0x1a184d6518>
png
From the matrix, you can see:
- LSTAT
- RM
Are strongly correlated with PRICE. Another exciting feature is the strong positive correlation between NOX and INDUS, which means those two variables move in the same direction. Besides, there are also correlated with the PRICE. DIS is also highly correlated with IND and NOX.
You have some first hint that IND and NOX can be good candidates for the interaction term and DIS might also be interesting to focus on.
You can go a little bit deeper by plotting a pair grid. It will illustrate more in detail the correlation map you plotted before.
The pair grid we are composed as follow:
- Upper part: Scatter plot with fitted line
- Diagonal: Kernel density plot
- Lower part: Multivariate kernel density plot
You choose the focus on four independent variables. The choice corresponds to the variables with strong correlation with PRICE
- INDUS
- NOX
- RM
- LSTAT
moreover, the PRICE.
Note that the standard error is added by default to the scatter plot.
attributes = ["PRICE", "INDUS", "NOX", "RM", "LSTAT"] g = sns.PairGrid(df[attributes]) g = g.map_upper(sns.regplot, color="g") g = g.map_lower(sns.kdeplot,cmap="Reds", shade=True, shade_lowest=False) g = g.map_diag(sns.kdeplot)
Output
Let's begin with the upper part:
- Price is negatively correlated with INDUS, NOX, and LSTAT; positively correlated with RM.
- There is a slightly non-linearity with LSTAT and PRICE
- There is like a straight line when the price is equal to 50. From the description of the dataset, PRICE has been truncated at the value of 50
Diagonal
- NOX seems to have two clusters, one around 0.5 and one around 0.85.
To check more about it, you can look at the lower part. The Multivariate Kernel Density is interesting in a sense it colors where most of the points are. The difference with the scatter plot draws a probability density, even though there is no point in the dataset for a given coordinate. When the color is stronger, it indicates a high concentration of point around this area.
If you check the multivariate density for INDUS and NOX, you can see the positive correlation and the two clusters. When the share of the industry is above 18, the nitric oxides concentration is above 0.6.
You can think about adding an interaction between INDUS and NOX in the linear regression.
Finally, you can use the second tools created by Google, Facets Deep Dive. The interface is divided up into four main sections. The central area in the center is a zoomable display of the data. On the top of the panel, there is the drop-down menu where you can change the arrangement of the data to controls faceting, positioning, and color. On the right, there is a detailed view of a specific row of data. It means you can click on any dot of data in the center visualization to see the detail about that particular data point.
During the data visualization step, you are interested in looking for the pairwise correlation between the independent variable on the price of the house. However, it involves at least three variables, and 3D plots are complicated to work with.
One way to tackle this problem is to create a categorical variable. That is, we can create a 2D plot a color the dot. You can split the variable PRICE into four categories, with each category is a quartile (i.e., 0.25, 0.5, 0.75). You call this new variable Q_PRICE.
## Check non linearity with important features df['Q_PRICE'] = pd.qcut(df['PRICE'], 4, labels=["Lowest", "Low", "Upper", "upper_plus"]) ## Show non linearity between RM and LSTAT ax = sns.lmplot(x="DIS", y="INDUS", hue="Q_PRICE", data=df, fit_reg = False,palette="Set3")
Facets Deep Dive
To open Deep Dive, you need to transform the data into a json format. Pandas as an object for that. You can use to_json after the Pandas dataset.
The first line of code handle the size of the dataset.
df['Q_PRICE'] = pd.qcut(df['PRICE'], 4, labels=["Lowest", "Low", "Upper", "upper_plus"]) sprite_size = 32 if len(df.index)>50000 else 64 jsonstr = df.to_json(orient='records')
The code below comes from Google GitHub. After you run the code, you should be able to see this:
# Display thde Dive visualization for this data
from IPython.core.display import display, HTML
# Create Facets template
HTML_TEMPLATE = """<link rel="import" href="/nbextensions/facets-dist/facets-jupyter.html">
<facets-dive sprite-image-width="{sprite_size}" sprite-image-height="{sprite_size}" id="elem" height="600"></facets-dive>
<script>
document.querySelector("#elem").data = {jsonstr};
</script>"""
# Load the json dataset and the sprite_size into the template
html = HTML_TEMPLATE.format(jsonstr=jsonstr, sprite_size=sprite_size)
# Display the template
display(HTML(html))
You are interested to see if there is a connection between the industry rate, oxide concentration, distance to the job center and the price of the house.
For that, you first split the data by industry range and color with the price quartile:
- Select faceting X and choose INDUS.
- Select Display and choose DIS. It will color the dots with the quartile of the house price
here, darker colors mean the distance to the first job center is far.
So far, it shows again what you know, lower industry rate, higher price. Now you can look at the breakdown by INDUX, by NOX.
- Select faceting Y and choose NOX.
Now you can see the house far from the first job center have the lowest industry share and therefore the lowest oxide concentration. If you choose to display the type with Q_PRICE and zoom the lower-left corner, you can see what type of price it is.
You have another hint that the interaction between IND, NOX, and DIS can be good candidates to improve the model.
TensorFlow
In this section, you will estimate the linear classifier with TensorFlow estimators API. You will proceed as follow:
- Prepare the data
- Estimate a benchmark model: No interaction
- Estimate a model with interaction
Remember, the goal of machine learning is the minimize the error. In this case, the model with the lowest mean square error will win. The TensorFlow estimator automatically computes this metric.
Preparation data
In most of the case, you need to transform your data. That is why Facets Overview is fascinating. From the summary statistic, you saw there are outliers. Those values affect the estimates because they do not look like the population you are analyzing. Outliers usually biased the results. For instance, a positive outlier tends to overestimate the coefficient.
A good solution to tackle this problem is to standardize the variable. Standardization means a standard deviation of one and means of zero. The process of standardization involves two steps. First of all, it subtracts the mean value of the variable. Secondly, it divides by the variance so that the distribution has a unit variance
The library sklearn is helpful to standardize variables. You can use the module preprocessing with the object scale for this purpose.
You can use the function below to scale a dataset. Note that you don't scale the label column and categorical variables.
from sklearn import preprocessing
def standardize_data(df):
X_scaled = preprocessing.scale(df[['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
'TAX', 'PTRATIO', 'B', 'LSTAT']])
X_scaled_df = pd.DataFrame(X_scaled, columns = ['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
'TAX', 'PTRATIO', 'B', 'LSTAT'])
df_scale = pd.concat([X_scaled_df,
df['CHAS'],
df['PRICE']],axis=1, join='inner')
return df_scale
You can use the function to construct the scaled train/test set.
df_train_scale = standardize_data(df_train) df_test_scale = standardize_data(df_test)
Basic regression:Benchmark
First of all, you train and test a model without interaction. The purpose is to see the performance metric of the model.
The way to train the model is exactly as the tutorial on High-level API. You will use the TensorFlow estimator LinearRegressor.
As a reminder, you need to choose:
- the features to put in the model
- transform the features
- construct the linear regressor
- construct the input_fn function
- train the model
- test the model
You use all the variables in the dataset to train the model. In total, there are elevel continuous variables and one categorical variable
## Add features to the bucket: ### Define continuous list CONTI_FEATURES = ['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD','TAX', 'PTRATIO', 'B', 'LSTAT'] CATE_FEATURES = ['CHAS']
You convert the features into a numeric column or categorical column
continuous_features = [tf.feature_column.numeric_column(k) for k in CONTI_FEATURES]
#categorical_features = tf.feature_column.categorical_column_with_hash_bucket(CATE_FEATURES, hash_bucket_size=1000)
categorical_features = [tf.feature_column.categorical_column_with_vocabulary_list('CHAS', ['yes','no'])]
You create the model with the linearRegressor. You store the model in the folder train_Boston
model = tf.estimator.LinearRegressor(
model_dir="train_Boston",
feature_columns=categorical_features + continuous_features)
Output
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': 'train_Boston', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1a19e76ac8>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
Each column in the train or test data is converted into a Tensor with the the function get_input_fn
FEATURES = ['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD','TAX', 'PTRATIO', 'B', 'LSTAT', 'CHAS']
LABEL= 'PRICE'
def get_input_fn(data_set, num_epochs=None, n_batch = 128, shuffle=True):
return tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
y = pd.Series(data_set[LABEL].values),
batch_size=n_batch,
num_epochs=num_epochs,
shuffle=shuffle)
You estimate the model on the train data.
model.train(input_fn=get_input_fn(df_train_scale,
num_epochs=None,
n_batch = 128,
shuffle=False),
steps=1000)
Output
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 1 into train_Boston/model.ckpt. INFO:tensorflow:loss = 56417.703, step = 1 INFO:tensorflow:global_step/sec: 144.457 INFO:tensorflow:loss = 76982.734, step = 101 (0.697 sec) INFO:tensorflow:global_step/sec: 258.392 INFO:tensorflow:loss = 21246.334, step = 201 (0.383 sec) INFO:tensorflow:global_step/sec: 227.998 INFO:tensorflow:loss = 30534.78, step = 301 (0.439 sec) INFO:tensorflow:global_step/sec: 210.739 INFO:tensorflow:loss = 36794.5, step = 401 (0.477 sec) INFO:tensorflow:global_step/sec: 234.237 INFO:tensorflow:loss = 8562.981, step = 501 (0.425 sec) INFO:tensorflow:global_step/sec: 238.1 INFO:tensorflow:loss = 34465.08, step = 601 (0.420 sec) INFO:tensorflow:global_step/sec: 237.934 INFO:tensorflow:loss = 12241.709, step = 701 (0.420 sec) INFO:tensorflow:global_step/sec: 220.687 INFO:tensorflow:loss = 11019.228, step = 801 (0.453 sec) INFO:tensorflow:global_step/sec: 232.702 INFO:tensorflow:loss = 24049.678, step = 901 (0.432 sec) INFO:tensorflow:Saving checkpoints for 1000 into train_Boston/model.ckpt. INFO:tensorflow:Loss for final step: 23228.568. <tensorflow.python.estimator.canned.linear.LinearRegressor at 0x1a19e76320>
At last, you estimate the performances of the model on the test set
model.evaluate(input_fn=get_input_fn(df_test_scale,
num_epochs=1,
n_batch = 128,
shuffle=False),
steps=1000)
Output
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-05-29-02:40:43
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from train_Boston/model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2018-05-29-02:40:43
INFO:tensorflow:Saving dict for global step 1000: average_loss = 86.89361, global_step = 1000, loss = 1650.9785
{'average_loss': 86.89361, 'global_step': 1000, 'loss': 1650.9785}
The loss of the model is 1650. This is the metric to beat in the next section
Improve the model: Interaction term
During the first part of the tutorial, you saw an interesting relationship between the variables. The different visualization techniques revealed that INDUS and NOS are linked together and turns to magnify the effect on the price. Not only the interaction between INDUS and NOS affects the price but also this effect is stronger when it interacts with DIS.
It is time to generalize this idea and see if you can improve the model prediction.
You need to add two new columns to each dataset set: train + test. For that, you create one function to compute the interaction term and another one to compute the triple interaction term. Each function produces a single column. After the new variables are created, you can concatenate them to the training dataset and test dataset.
First of all, you need to create a new variable for the interaction between INDUS and NOX.
The function below returns two dataframes, train and test, with the interaction between var_1 and var_2, in your case INDUS and NOX.
def interaction_term(var_1, var_2, name):
t_train = df_train_scale[var_1]*df_train_scale[var_2]
train = t_train.rename(name)
t_test = df_test_scale[var_1]*df_test_scale[var_2]
test = t_test.rename(name)
return train, test
You store the two new columns
interation_ind_ns_train, interation_ind_ns_test= interaction_term('INDUS', 'NOX', 'INDUS_NOS')
interation_ind_ns_train.shape
(325,)
Secondly, you create a second function to compute the triple interaction term.
def triple_interaction_term(var_1, var_2,var_3, name):
t_train = df_train_scale[var_1]*df_train_scale[var_2]*df_train_scale[var_3]
train = t_train.rename(name)
t_test = df_test_scale[var_1]*df_test_scale[var_2]*df_test_scale[var_3]
test = t_test.rename(name)
return train, test
interation_ind_ns_dis_train, interation_ind_ns_dis_test= triple_interaction_term('INDUS', 'NOX', 'DIS','INDUS_NOS_DIS')
Now that you have all columns needed, you can add them to train and test dataset. You name these two new dataframe:
- df_train_new
- df_test_new
df_train_new = pd.concat([df_train_scale,
interation_ind_ns_train,
interation_ind_ns_dis_train],
axis=1, join='inner')
df_test_new = pd.concat([df_test_scale,
interation_ind_ns_test,
interation_ind_ns_dis_test],
axis=1, join='inner')
df_train_new.head(5)
Output
That is it; you can estimate the new model with the interaction terms and see how is the performance metric.
CONTI_FEATURES_NEW = ['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD','TAX', 'PTRATIO', 'B', 'LSTAT',
'INDUS_NOS', 'INDUS_NOS_DIS']
### Define categorical list
continuous_features_new = [tf.feature_column.numeric_column(k) for k in CONTI_FEATURES_NEW]
model = tf.estimator.LinearRegressor(
model_dir="train_Boston_1",
feature_columns= categorical_features + continuous_features_new)
Output
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': 'train_Boston_1', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1a1a5d5860>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
CODE
FEATURES = ['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD','TAX', 'PTRATIO', 'B', 'LSTAT','INDUS_NOS', 'INDUS_NOS_DIS','CHAS']
LABEL= 'PRICE'
def get_input_fn(data_set, num_epochs=None, n_batch = 128, shuffle=True):
return tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
y = pd.Series(data_set[LABEL].values),
batch_size=n_batch,
num_epochs=num_epochs,
shuffle=shuffle)
model.train(input_fn=get_input_fn(df_train_new,
num_epochs=None,
n_batch = 128,
shuffle=False),
steps=1000)
Output
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 1 into train_Boston_1/model.ckpt. INFO:tensorflow:loss = 56417.703, step = 1 INFO:tensorflow:global_step/sec: 124.844 INFO:tensorflow:loss = 65522.3, step = 101 (0.803 sec) INFO:tensorflow:global_step/sec: 182.704 INFO:tensorflow:loss = 15384.148, step = 201 (0.549 sec) INFO:tensorflow:global_step/sec: 208.189 INFO:tensorflow:loss = 22020.305, step = 301 (0.482 sec) INFO:tensorflow:global_step/sec: 213.855 INFO:tensorflow:loss = 28208.812, step = 401 (0.468 sec) INFO:tensorflow:global_step/sec: 209.758 INFO:tensorflow:loss = 7606.877, step = 501 (0.473 sec) INFO:tensorflow:global_step/sec: 196.618 INFO:tensorflow:loss = 26679.76, step = 601 (0.514 sec) INFO:tensorflow:global_step/sec: 196.472 INFO:tensorflow:loss = 11377.163, step = 701 (0.504 sec) INFO:tensorflow:global_step/sec: 172.82 INFO:tensorflow:loss = 8592.07, step = 801 (0.578 sec) INFO:tensorflow:global_step/sec: 168.916 INFO:tensorflow:loss = 19878.56, step = 901 (0.592 sec) INFO:tensorflow:Saving checkpoints for 1000 into train_Boston_1/model.ckpt. INFO:tensorflow:Loss for final step: 19598.387. <tensorflow.python.estimator.canned.linear.LinearRegressor at 0x1a1a5d5e10>
model.evaluate(input_fn=get_input_fn(df_test_new,
num_epochs=1,
n_batch = 128,
shuffle=False),
steps=1000)
Output
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-05-29-02:41:14
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from train_Boston_1/model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2018-05-29-02:41:14
INFO:tensorflow:Saving dict for global step 1000: average_loss = 79.78876, global_step = 1000, loss = 1515.9863
{'average_loss': 79.78876, 'global_step': 1000, 'loss': 1515.9863}
The new loss is 1515. Just by adding two new variables, you were able to decrease the loss. It means you can make a better prediction than with the benchmark model.