In [1]:
%matplotlib inline
In [2]:
import os
import sys
# Modify the path
sys.path.append("..")
import numpy as np
import pandas as pd
import yellowbrick as yb
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams["figure.figsize"] = (9,6)
In [3]:
g = yb.anscombe()
Yellowbrick has provided several datasets wrangled from the UCI Machine Learning Repository to present the following examples. If you haven't downloaded the data, you can do so by running:
$ python download.pyIn the same directory as the example notebook. Note that this will create a directory called data that contains subdirectories with the given data.
In [4]:
from download import download_all
## The path to the test data sets
FIXTURES = os.path.join(os.getcwd(), "data")
## Dataset loading mechanisms
datasets = {
"credit": os.path.join(FIXTURES, "credit", "credit.csv"),
"concrete": os.path.join(FIXTURES, "concrete", "concrete.csv"),
"occupancy": os.path.join(FIXTURES, "occupancy", "occupancy.csv"),
"mushroom": os.path.join(FIXTURES, "mushroom", "mushroom.csv"),
}
def load_data(name, download=True):
"""
Loads and wrangles the passed in dataset by name.
If download is specified, this method will download any missing files.
"""
# Get the path from the datasets
path = datasets[name]
# Check if the data exists, otherwise download or raise
if not os.path.exists(path):
if download:
download_all()
else:
raise ValueError((
"'{}' dataset has not been downloaded, "
"use the download.py module to fetch datasets"
).format(name))
# Return the data frame
return pd.read_csv(path)
Feature analysis visualizers are designed to visualize instances in data space in order to detect features or targets that might impact downstream fitting. Because ML operates on high-dimensional data sets (usually at least 35), the visualizers focus on aggregation, optimization, and other techniques to give overviews of the data. It is our intent that the steering process will allow the data scientist to zoom and filter and explore the relationships between their instances and between dimensions.
At the moment, we have the following feature analysis visualizers available:
Feature analysis visualizers implement the Transformer API from scikit-learn, meaning they can be used as intermediate transform steps in a Pipeline (particularly a VisualPipeline).
They are instantiated in the same way, and then fit and transform are called on them, which draws the instances correctly. Finally show or show is called which displays the image.
In [5]:
# Feature Analysis Imports
from yellowbrick.features import JointPlotVisualizer
from yellowbrick.features import ParallelCoordinates
from yellowbrick.features import PCADecomposition
from yellowbrick.features import Rank1D, Rank2D
from yellowbrick.features import RadViz
Rank1D and Rank2D evaluate single features or pairs of features using a variety of metrics that score the features on the scale [-1, 1] or [0, 1] allowing them to be ranked. A similar concept to SPLOMs, the scores are visualized on a lower-left triangle heatmap so that patterns between pairs of features can be easily discerned for downstream analysis.
In [6]:
# Load the classification data set
data = load_data('credit')
# Specify the features of interest
features = [
'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',
'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',
'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',
'jul_pay', 'aug_pay', 'sep_pay',
]
# Extract the numpy arrays from the data frame
X = data[features]
y = data.default
In [7]:
# Instantiate the visualizer with the Covariance ranking algorithm
visualizer = Rank1D(features=features, algorithm='shapiro')
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Finalize and render the figure
In [8]:
# Instantiate the visualizer with the Covariance ranking algorithm
visualizer = Rank2D(features=features, algorithm='covariance')
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Finalize and render the visualizer
In [9]:
# Instantiate the visualizer with the Pearson ranking algorithm
visualizer = Rank2D(features=features, algorithm='pearson')
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Finalize and render the visualizer
RadViz is a multivariate data visualization algorithm that plots each feature dimension uniformly around the circumference of a circle then plots points on the interior of the circle such that the point normalizes its values on the axes from the center to each arc. This mechanism allows as many dimensions as will easily fit on a circle, greatly expanding the dimensionality of the visualization.
Data scientists use this method to detect separability between classes. E.g. is there an opportunity to learn from the feature set or is there just too much noise?
In [10]:
# Load the classification data set
data = load_data('occupancy')
# Specify the features of interest and the classes of the target
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features]
y = data.occupancy
In [11]:
# Instantiate the visualizer
visualizer = visualizer = RadViz(classes=classes, features=features)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Finalize and render the visualizer
For regression, the RadViz visualizer should use a color sequence to display the target information, as opposed to discrete colors.
Parallel coordinates displays each feature as a vertical axis spaced evenly along the horizontal, and each instance as a line drawn between each individual axis. This allows many dimensions; in fact given infinite horizontal space (e.g. a scrollbar) an infinite number of dimensions can be displayed!
Data scientists use this method to detect clusters of instances that have similar classes, and to note features that have high variance or different distributions.
In [12]:
# Load the classification data set
data = load_data('occupancy')
# Specify the features of interest and the classes of the target
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features]
y = data.occupancy
In [13]:
# Instantiate the visualizer
visualizer = ParallelCoordinates(classes=classes, features=features)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Finalize and render the visualizer
In [14]:
# Instantiate the visualizer
visualizer = visualizer = ParallelCoordinates(
classes=classes, features=features,
normalize='standard', sample=0.1,
)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Finalize and render the visualizer
In [15]:
# Load the concrete data
df = load_data('concrete')
# Specify the feature and target variables
feature = 'cement'
target = 'strength'
# Extract the instance data and the target
X = df[feature]
y = df[target]
In [16]:
# Instantiate the visualizer
visualizer = JointPlotVisualizer(feature=feature, target=target)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.show() # Finalize and render the visualizer
In [17]:
# Load the classification data set
data = load_data('credit')
# Specify the features of interest
features = [
'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',
'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',
'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',
'jul_pay', 'aug_pay', 'sep_pay',
]
# Extract the numpy arrays from the data frame
X = data[features]
y = data.default
In [18]:
visualizer = PCADecomposition(scale=True, center=False, col=y)
visualizer.fit_transform(X,y)
visualizer.show()
In [19]:
visualizer = PCADecomposition(scale=True, center=False, col=y, proj_dim=3)
visualizer.fit_transform(X,y)
visualizer.show()
The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.
Estimator score visualizers wrap scikit-learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline.
In machine learning, regression models attempt to predict a target in a continuous space. Yellowbrick has implemented the following regressor score visualizers that display the instances in model space to better understand how the model is making predictions:
AlphaSelection visual tuning of regularization hyperparametersPredictionError plot the expected vs. the actual values in model space Residuals Plot plot the difference between the expected and actual values
In [20]:
# Regression Evaluation Imports
from sklearn.linear_model import Lasso, LassoCV, Ridge, RidgeCV
from sklearn.model_selection import cross_val_predict, train_test_split
from yellowbrick.datasets import load_concrete
from yellowbrick.regressor import AlphaSelection, PredictionError, ResidualsPlot
Yellowbrick provides several datasets wrangled from the UCI Machine Learning Repository. For the following examples, we'll use the concrete dataset, since it is well-suited for regression tasks.
The concrete dataset contains 1030 instances and 9 attributes. Eight of the attributes are explanatory variables, including the age of the concrete and the materials used to create it, while the target variable strength is a measure of the concrete's compressive strength (MPa).
In [21]:
# Use Yellowbrick to load the concrete dataset
data = load_concrete()
# Save the data in a Pandas DataFrame
df = pd.DataFrame(data['data'], columns=data['feature_names'], dtype='float')
# Save feature names as a list and target variable as a string
feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']
target_name = 'strength'
# Get the X and y data from the DataFrame
X = df[feature_names]
y = df[target_name]
# Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
A residual is the difference between the observed value of the target variable (y) and the predicted value (ŷ), i.e. the error of the prediction. The ResidualsPlot Visualizer shows the difference between residuals on the vertical axis and the dependent variable on the horizontal axis, allowing you to detect regions within the target that may be susceptible to more or less error.
If the points are randomly dispersed around the horizontal axis, a linear regression model is usually well-suited for the data; otherwise, a non-linear model is more appropriate. The following example shows a fairly random, uniform distribution of the residuals against the target in two dimensions. This seems to indicate that our linear model is performing well.
In [22]:
# Instantiate the linear model and visualizer
model = Ridge()
visualizer = ResidualsPlot(model)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Finalize and render the visualizer
Yellowbrick's PredictionError Visualizer plots the actual targets from the dataset against the predicted values generated by the model. This allows us to see how much variance is in the model. Data scientists can diagnose regression models using this plot by comparing against the 45-degree line, where the prediction exactly matches the model.
In [23]:
# Instantiate the linear model and visualizer
model = Lasso()
visualizer = PredictionError(model)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Finalize and render the visualizer
The AlphaSelection Visualizer demonstrates how different values of alpha influence model selection during the regularization of linear models. Since regularization is designed to penalize model complexity, the higher the alpha, the less complex the model, decreasing the error due to variance (overfit). However, alphas that are too high increase the error due to bias (underfit). Therefore, it is important to choose an optimal alpha such that the error is minimized in both directions.
To do this, typically you would you use one of the "RegressionCV” models in scikit-learn. E.g. instead of using the Ridge (L2) regularizer, use RidgeCV and pass a list of alphas, which will be selected based on the cross-validation score of each alpha. This visualizer wraps a “RegressionCV” model and visualizes the alpha/error curve. If the visualization shows a jagged or random plot, then potentially the model is not sensitive to that type of regularization and another is required (e.g. L1 or Lasso regularization).
In [24]:
# Create a list of alphas to cross-validate against
alphas = np.logspace(-10, 1, 400)
# Instantiate the linear model and visualizer
model = LassoCV(alphas=alphas)
visualizer = AlphaSelection(model)
visualizer.fit(X, y) # Fit the data to the visualizer
g = visualizer.show() # Finalize and render the visualizer
Classification models attempt to predict a target in a discrete space, that is assign an instance of dependent variables one or more categories. Classification score visualizers display the differences between classes as well as a number of classifier-specific visual evaluations. We currently have implemented three classifier evaluations:
Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline.
In [25]:
# Classifier Evaluation Imports
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance, ConfusionMatrix
In [26]:
# Load the classification data set
data = load_data('occupancy')
# Specify the features of interest and the classes of the target
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features].as_matrix()
y = data.occupancy.as_matrix()
# Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
In [27]:
# Instantiate the classification model and visualizer
bayes = GaussianNB()
visualizer = ClassificationReport(bayes, classes=classes)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Finalize and render the visualizer
The ConfusionMatrix visualizer displays the accuracy score of the model, i.e., it shows how each of the test values predicted classes compare to their actual classes. It provides the numerical scores as well as a color-coded heatmap to provide data scientists a clearer view of the model performance on each of the individual classes and is particularly useful for imbalanced datasets. More information can be found by looking at the scikit-learn documentation.
In [28]:
# Instantiate the classification model and visualizer
bayes = GaussianNB()
visualizer = ConfusionMatrix(bayes)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Finalize and render the visualizer
In [29]:
# Load the classification data set
data = load_data('occupancy')
# Specify the features of interest and the classes of the target
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features].as_matrix()
y = data.occupancy.as_matrix()
# Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
In [30]:
# Instantiate the classification model and visualizer
logistic = LogisticRegression()
visualizer = ROCAUC(logistic)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Finalize and render the visualizer
In [31]:
# Load the classification data set
data = load_data('occupancy')
# Specify the features of interest and the classes of the target
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']
# Extract the numpy arrays from the data frame
X = data[features].as_matrix()
y = data.occupancy.as_matrix()
# Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
In [32]:
# Instantiate the classification model and visualizer
forest = RandomForestClassifier()
visualizer = ClassBalance(forest, classes=classes)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Finalize and render the visualizer
The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the Visualizer. Visualizers allow models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.
In machine learning, clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithms: agglomerative clustering which links similar data points together, and centroidal clustering which attempts to find centers or partitions in the data.
Currently, Yellowbrick provides two visualizers to evaluate centroidal mechanisms, particularly K-Means clustering, that help users discover an optimal $K$ parameter in the clustering metric:
KElbowVisualizer visualizes the clusters according to a scoring function, looking for an "elbow" in the curve. SilhouetteVisualizer visualizes the silhouette scores of each cluster in a single model.
In [33]:
# Clustering Evaluation Imports
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
In [34]:
# Generate synthetic dataset with 8 blobs
X, y = make_blobs(n_samples=1000, n_features=12, centers=8, shuffle=True, random_state=42)
K-Means is a simple unsupervised machine learning algorithm that groups data into the number $K$ of clusters specified by the user, even if it is not the optimal number of clusters for the dataset.
Yellowbrick's KElbowVisualizer implements the “elbow” method of selecting the optimal number of clusters by fitting the K-Means model with a range of values for $K$. If the line chart looks like an arm, then the “elbow” (the point of inflection on the curve) is a good indication that the underlying model fits best at that point.
In the following example, the KElbowVisualizer fits the model for a range of $K$ values from 4 to 11, which is set by the parameter k=(4,12). When the model is fit with 8 clusters we can see an "elbow" in the graph, which in this case we know to be the optimal number since we created our synthetic dataset with 8 clusters of points.
In [35]:
# Instantiate the clustering model and visualizer
model = KMeans()
visualizer = KElbowVisualizer(model, k=(4,12))
visualizer.fit(X) # Fit the data to the visualizer
visualizer.show() # Finalize and render the visualizer
Silhouette analysis can be used to evaluate the density and separation between clusters. The score is calculated by averaging the silhouette coefficient for each sample, which is computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between -1 and +1, where scores near +1 indicate high separation and scores near -1 indicate that the samples may have been assigned to the wrong cluster.
The SilhouetteVisualizer displays the silhouette coefficient for each sample on a per-cluster basis, allowing users to visualize the density and separation of the clusters. This is particularly useful for determining cluster imbalance or for selecting a value for $K$ by comparing multiple visualizers.
In [36]:
# Instantiate the clustering model and visualizer
model = KMeans(6)
visualizer = SilhouetteVisualizer(model)
visualizer.fit(X) # Fit the data to the visualizer
visualizer.show() # Finalize and render the visualizer
Yellowbrick provides the yellowbrick.text module for text-specific visualizers. The TextVisualizer class specifically deals with datasets that are corpora and not simple numeric arrays or DataFrames, providing utilities for analyzing word distribution, showing document similarity, or simply wrapping some of the other standard visualizers with text-specific display properties.
As in the previous sections, Yellowbrick has provided a sample dataset to run the following cells. In particular, we are going to use a text corpus wrangled from the Baleen RSS Corpus to present the following examples. If you haven't already downloaded the data, you can do so by running:
$ python download.pyIn the same directory as the examples notebook. Note that this will create a directory called data that contains subdirectories with the provided datasets.
NOTE: If you've already done this from above, you do not have to do it again.
In [37]:
from download import download_all
from sklearn.datasets.base import Bunch
## The path to the test data sets
FIXTURES = os.path.join(os.getcwd(), "data")
## Corpus loading mechanisms
corpora = {
"hobbies": os.path.join(FIXTURES, "hobbies")
}
def load_corpus(name, download=True):
"""
Loads and wrangles the passed in text corpus by name.
If download is specified, this method will download any missing files.
Note: This function is slightly different to the `load_data` function
used above to load pandas dataframes into memory.
"""
# Get the path from the datasets
path = corpora[name]
# Check if the data exists, otherwise download or raise
if not os.path.exists(path):
if download:
download_all()
else:
raise ValueError((
"'{}' dataset has not been downloaded, "
"use the download.py module to fetch datasets"
).format(name))
# Read the directories in the directory as the categories.
categories = [
cat for cat in os.listdir(path)
if os.path.isdir(os.path.join(path, cat))
]
files = [] # holds the file names relative to the root
data = [] # holds the text read from the file
target = [] # holds the string of the category
# Load the data from the files in the corpus
for cat in categories:
for name in os.listdir(os.path.join(path, cat)):
files.append(os.path.join(path, cat, name))
target.append(cat)
with open(os.path.join(path, cat, name), 'r') as f:
data.append(f.read())
# Return the data bunch for use similar to the newsgroups example
return Bunch(
categories=categories,
files=files,
data=data,
target=target,
)
One very popular method for visualizing document similarity is to use t-distributed stochastic neighbor embedding, t-SNE. Scikit-Learn implements this decomposition method as the sklearn.manifold.TSNE transformer. By decomposing high-dimensional document vectors into 2 dimensions using probability distributions from both the original dimensionality and the decomposed dimensionality, t-SNE is able to effectively cluster similar documents. By decomposing to 2 or 3 dimensions, the documents can be visualized with a scatter plot.
Unfortunately, TSNE is very expensive, so typically a simpler decomposition method such as SVD or PCA is applied ahead of time. The TSNEVisualizer creates an inner transformer pipeline that applies such a decomposition first (SVD with 50 components by default), then performs the t-SNE embedding. The visualizer then plots the scatter plot, coloring by cluster or by class, or neither if a structural analysis is required.
In [38]:
from yellowbrick.text import TSNEVisualizer
from sklearn.feature_extraction.text import TfidfVectorizer
In [39]:
# Load the data and create document vectors
corpus = load_corpus('hobbies')
tfidf = TfidfVectorizer()
docs = tfidf.fit_transform(corpus.data)
labels = corpus.target
In [40]:
# Create the visualizer and draw the vectors
tsne = TSNEVisualizer()
tsne.fit(docs, labels)
tsne.show()
In [41]:
# Only visualize the sports, cinema, and gaming classes
tsne = TSNEVisualizer(classes=['sports', 'cinema', 'gaming'])
tsne.fit(docs, labels)
tsne.show()
In [42]:
# Don't color points with their classes
tsne = TSNEVisualizer()
tsne.fit(docs)
tsne.show()
In [43]:
# Apply clustering instead of class names.
from sklearn.cluster import KMeans
clusters = KMeans(n_clusters=5)
clusters.fit(docs)
tsne = TSNEVisualizer()
tsne.fit(docs, ["c{}".format(c) for c in clusters.labels_])
tsne.show()
A method for visualizing the frequency of tokens within and across corpora is frequency distribution. A frequency distribution tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable event. It is a distribution because it tells us how the total number of word tokens in the text are distributed across the vocabulary items.
In [44]:
from yellowbrick.text.freqdist import FreqDistVisualizer
from sklearn.feature_extraction.text import CountVectorizer
Note that the FreqDistVisualizer does not perform any normalization or vectorization, and it expects text that has already be count vectorized.
We first instantiate a FreqDistVisualizer object, and then call fit() on that object with the count vectorized documents and the features (i.e. the words from the corpus), which computes the frequency distribution. The visualizer then plots a bar chart of the top 50 most frequent terms in the corpus, with the terms listed along the x-axis and frequency counts depicted at y-axis values. As with other Yellowbrick visualizers, when the user invokes show(), the finalized visualization is shown.
In [45]:
vectorizer = CountVectorizer()
docs = vectorizer.fit_transform(corpus.data)
features = vectorizer.get_feature_names()
visualizer = FreqDistVisualizer(features=features)
visualizer.fit(docs)
visualizer.show()
In [46]:
vectorizer = CountVectorizer(stop_words='english')
docs = vectorizer.fit_transform(corpus.data)
features = vectorizer.get_feature_names()
visualizer = FreqDistVisualizer(features=features)
visualizer.fit(docs)
visualizer.show()
It is also interesting to explore the differences in tokens across a corpus. The hobbies corpus that comes with Yellowbrick has already been categorized (try corpus['categories']), so let's visually compare the differences in the frequency distributions for two of the categories: "cooking" and "gaming"
In [47]:
hobby_types = {}
for category in corpus['categories']:
texts = []
for idx in range(len(corpus['data'])):
if corpus['target'][idx] == category:
texts.append(corpus['data'][idx])
hobby_types[category] = texts
In [48]:
vectorizer = CountVectorizer(stop_words='english')
docs = vectorizer.fit_transform(text for text in hobby_types['cooking'])
features = vectorizer.get_feature_names()
visualizer = FreqDistVisualizer(features=features)
visualizer.fit(docs)
visualizer.show()
In [49]:
vectorizer = CountVectorizer(stop_words='english')
docs = vectorizer.fit_transform(text for text in hobby_types['gaming'])
features = vectorizer.get_feature_names()
visualizer = FreqDistVisualizer(features=features)
visualizer.fit(docs)
visualizer.show()