![]() ![]() For this it transforms each column depending on its data type. ![]() class SuperVectorizer ( ColumnTransformer ): """ Easily transforms a heterogeneous data table (such as a dataframe) to a numerical array for machine learning. fillna ( value = value ) return ser OptionalTransformer = Optional ] ] is_categorical_dtype ( ser ) and ( value not in ser. """ ser = _replace_false_missing ( ser ) if pd. Series : """ Takes a Series with string data, replaces the missing values, and returns it. Series, value : str = "missing" ) -> pd. nan, regex = True ) # Replace whitespaces return df def _replace_missing_in_cat_col ( ser : pd. STR_NA_VALUES = # taken from pandas.io.parsers (version 1.1.4) df = df. """ # Should not replace "missing" (the string used for imputation in # categorical features). isnull ()) def _replace_false_missing ( df : Union ) -> Union : """ Takes a DataFrame or a Series, and replaces the "false missing", that is, strings that designate a missing value, but do not have the corresponding type. """ from typing import Dict, List, Literal, Optional, Tuple, Union from warnings import warn import numpy as np import pandas as pd import sklearn from import ExtensionDtype from sklearn import _version_ as sklearn_version from sklearn.base import TransformerMixin, clone from pose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder from dirty_cat import DatetimeEncoder, GapEncoder from dirty_cat.utils import Version def _has_missing_values ( df : Union ) -> bool : """ Returns True if `array` contains missing values, False otherwise. text = coun_vect = CountVectorizer(binary=True) count_matrix = coun_vect.fit_transform(text) count_array = count_matrix.toarray() df = pd.DataFrame(data=count_array,columns = coun_vect.""" Implements the SuperVectorizer: a preprocessor to automatically apply transformers/encoders to different types of data, without the need to manually categorize them beforehand, or construct complex Pipelines. This is usually used when the count of the term/word does not provide useful information to the machine learning model. text = coun_vect = CountVectorizer(max_features=3) count_matrix = coun_vect.fit_transform(text) count_array = count_matrix.toarray() df = pd.DataFrame(data=count_array,columns = coun_vect.get_feature_names()) print(df) dfīy setting ‘binary = True’, the CountVectorizer no more takes into consideration the frequency of the term/word. It takes absolute values so if you set the ‘max_features = 3’, it will select the 3 most common words in the data. The CountVectorizer will select the words/features/terms which occur the most frequently. Using absolute values: text = coun_vect = CountVectorizer( min_df=2) count_matrix = coun_vect.fit_transform(text) count_array = count_matrix.toarray() df = pd.DataFrame(data=count_array,columns = coun_vect.get_feature_names()) print(df) min_df can take absolute values(1,2,3.) or a value representing a percentage of documents(0.50, ignore words appearing in 50% of documents) For example in your dataset you may have names that appear in only 1 or 2 documents, now these could be ignored as they do not provide enough information on the entire dataset as a whole but only a couple of particular documents. When building the vocabulary Min_df ignores terms that have a document frequency strictly lower than the given threshold. Min_df stands for minimum document frequency, as opposed to term frequency which counts the number of times the word has occurred in the entire dataset, document frequency counts the number of documents in the dataset (aka rows or entries) that have the particular word. The words ‘is’, ‘to’, ‘james’, ‘my’ and ‘of’ have been removed from the sparse matrix as they occur in more than 1 document. Using absolute values: text = coun_vect = CountVectorizer( max_df=1) count_matrix = coun_vect.fit_transform(text) count_array = count_matrix.toarray() df = pd.DataFrame(data=count_array,columns = coun_vect.get_feature_names()) print(df) This parameter can again 2 types of values, percentage and absolute. Max_df looks at how many documents contain the word and if it exceeds the max_df threshold then it is eliminated from the sparse matrix. These words could be like the word ‘the’ that occur in every document and does not provide and valuable information to our text classification or any other machine learning model and can be safely ignored. Similar to min_df, we can ignore words which occur frequently. Max_df stands for maximum document frequency. ![]()
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