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Example Class with Singleton State and Fluent API
import numpy as np
import pandas as pd
import sklearn
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
class Content():
_cached_data = None
def __init__(self):
def _refresh_data_cache(self):
if Content._cached_data is None:
# extract my data into a dataframe. I'm pulling from a file,
# but you might do this by fetching data from an endpoint
# or pulling it from a database.
df = pd.read_csv('all_my_data.csv')
# example data cleaning. I'm dropping nulls and filling in defaults,
# but you might do things like calculate means
# or do other preprocessing steps you know you will want for later.
df = df.dropna(subset=['column_where_null_values_render_row_useless_for_analysis'])
df['column_where_null_values_mess_up_my_analysis'] = df['column_where_null_values_mess_up_my_analysis'].fillna('')
Content._cached_data = df
# example method that does additional computationally-expensive pre-processing on the data.
# I made a separate method for this instead of the 'cheap' pre-processing like on lines 24 and 25
# because it takes a while (15 seconds). So we only invest that time if the client explicitly _needs_ this done.
def tfidf_encoded(self):
if 'tfidf' not in Content._cached_data.columns:
df = Content._cached_data
base_contents = np.array(df['base_content'])
vocab_length = 40000
tfidf_transformer = TfidfVectorizer(ngram_range = (1,3), max_features = vocab_length)
tfidf_encodings = tfidf_transformer.fit_transform(base_contents)
df['tfidf'] = list(tfidf_encodings.toarray())
Content._cached_data = df
# Fluent interface methods return the object calling the method so the client can chain methods together.
return self
# In the 'original' fluent interfaces in Java, this method is often named .get().
# I need it because I want the value at the end of my fluent statement to be a dataframe, not a Content object,
# but initializers must return None (so Python can give back the initialized object we initialized).
# This method hands over the internal state as a dataframe.
def to_dataframe(self):
return Content._cached_data.copy() # Many thanks to Bijay Gurung for catching an issue with this! Now updated to work properly :)
#You might note that the above method is not needed in the pandas fluent interface.
#That's because the pandas methods that return dataframes are _not_ initializers.
#pd.Dataframe(), pd.read_csv(), and pd.read_excel() are all _class_ methods that return a dataframe object.
#There is no "pd" instance. This setup is convenient for users, but it's tough to mock and test.
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