Order, hue_order, etc.) to set up the plot correctly. Use alter the dataframe sorting or use the function parameters ( orient, Their order will be inferred from the objects. Passed in a long-form DataFrame with variables specified by passing stringsĪs in the case with the underlying plot functions, if variables have aĬategorical data type, the levels of the categorical variables, and Note that unlike when using the axes-level functions directly, data must be Should refer to the documentation for each to see kind-specific options. Stripplot() (with kind="strip" the default)Įxtra keyword arguments are passed to the underlying function, so you Parameter selects the underlying axes-level function to use: Variables using one of several visual representations. Show the relationship between a numerical and one or more categorical This function provides access to several axes-level functions that ![]() catplot ( *, x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=, ci=95, n_boot=1000, units=None, seed=None, order=None, hue_order=None, row_order=None, col_order=None, kind='strip', height=5, aspect=1, orient=None, color=None, palette=None, legend=True, legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None, **kwargs ) ¶įigure-level interface for drawing categorical plots onto a FacetGrid. Let’s extract more relevant columns to another dataframe: columns = g_summary = g_search_results].copy() g_lumns = columns = if '_' in col else col for col in g_lumns ] g_lumns = columns g_ ¶ seaborn. Like before, the output will be saved to a dataframe called g_search_results. Grid searching will also take a bit of time because we have 24 different combinations of hyperparameters to try. Once saved, let’s import it to Python: sample = pd.read_csv('IMDB Dataset.csv') print(f" g_search = GridSearchCV(estimator=pipe, param_grid=param_grid, cv=5, n_jobs=-1) g_search.fit(X_train, y_train) # Save results to a dataframe g_search_results = pd.DataFrame(g_search.cv_results_).sort_values(by='rank_test_score') You can download the dataset here and save it in your working directory. Now, we are ready to import the packages: # Set random seed seed = 123 # Data manipulation/analysis import numpy as np import pandas as pd # Text preprocessing/analysis import re from rpus import stopwords from nltk.stem import WordNetLemmatizer from nltk.tokenize import RegexpTokenizer from import SentimentIntensityAnalyzer from textblob import TextBlob from scipy.sparse import hstack, csr_matrix from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.preprocessing import MinMaxScaler # Modelling from sklearn.model_selection import train_test_split, cross_validate, GridSearchCV, RandomizedSearchCV from sklearn.linear_model import LogisticRegression, SGDClassifier from sklearn.naive_bayes import MultinomialNB from trics import classification_report, confusion_matrix from sklearn.pipeline import Pipeline # Visualisation import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline sns.set(style="whitegrid", context='talk') 1. If you have already downloaded, running this will notify you so. Once you have nltk installed, please make sure you have downloaded ‘stopwords’, ‘wordnet’ and ‘vader_lexicon’ from nltk with the script below: import nltk nltk.download('stopwords') nltk.download('wordnet') nltk.download('vader_lexicon') Let’s make sure you have the following libraries installed before we start: ◼️ Data manipulation/analysis: numpy, pandas ◼️ Data partitioning: sklearn ◼️ Text preprocessing/analysis: nltk, textblob ◼️ Visualisation: matplotlib, seaborn I have tested the scripts in Python 3.7.1 in Jupyter Notebook. ![]() ![]() If you are new to Python, this is a good place to get started. ![]() This post assumes that the reader (□ yes, you!) has access to and is familiar with Python including installing packages, defining functions and other basic tasks.
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