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| # Grabbing the preprocessor | |
| pre = fit_model.named_steps['preprocessor'] | |
| # Getting the numerical and categorical features from the pipeline | |
| num_feats = pre.transformers_[0][2] | |
| cat_feats = pre.transformers_[1][1]['onehot']\ | |
| .get_feature_names(categorical_features) | |
| all_feats = num_feats+list(cat_feats) | |
| # Dataframe for visual examination of coefficients |
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| # Generated as example for Springboard mentees | |
| import pandas as pd | |
| df = pd.DataFrame() | |
| df['code'] = ['1', '1', '2', '3', '3', '3', '3', '4', '4'] | |
| df['country'] = ['usa', '', 'france', 'japan', 'japan', '', 'japan', 'brazil', 'brazil'] | |
| df['extracolumn'] = ['i', 'do', 'not', 'need', 'the', 'stuff', 'in', 'this', 'column'] | |
| new_df = df[['code', 'country']].drop_duplicates() | |
| new_df = new_df[new_df['country'] != ''] | |
| new_df |
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| cd <path_to_directory_containing_files> && for file in *<file_type>; do mv "$file" "${file:<number_of_leading_characters_to_remove>}"; done |
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| cosine_similarity_vec <- function(row_index, df){ | |
| row <- df[row_index,] | |
| mat <- df[-row_index,] | |
| numerator <- rowSums(sweep(mat, MARGIN=2, row, "*")) | |
| denominator <- sqrt(sum(row**2)) * sqrt(rowSums(mat**2)) | |
| similarities <- numerator/denominator | |
| game_numbers <- 1:dim(df)[1] | |
| game_numbers <- game_numbers[! game_numbers %in% row_index] | |
| df_similarity <- data.frame(game_numbers, similarities) | |
| df_similarity <- df_similarity %>% arrange(desc(similarities)) |
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| #doc2vec parameters | |
| vector_size = 300 | |
| window_size = 15 | |
| min_count = 1 | |
| sampling_threshold = 1e-5 | |
| negative_size = 5 | |
| train_epoch = 100 | |
| dm = 0 #0 = dbow; 1 = dmpv | |
| worker_count = 1 #number of parallel processes |
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| #!/usr/bin/env python | |
| # encoding: utf-8 | |
| import tweepy #https://github.com/tweepy/tweepy | |
| import csv | |
| #Twitter API credentials | |
| consumer_key = "" | |
| consumer_secret = "" | |
| access_key = "" |
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| cat mtcars.csv | |
| "","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb" | |
| "Mazda RX4",21,6,160,110,3.9,2.62,16.46,0,1,4,4 | |
| "Mazda RX4 Wag",21,6,160,110,3.9,2.875,17.02,0,1,4,4 | |
| "Datsun 710",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1 | |
| "Hornet 4 Drive",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1 | |
| "Hornet Sportabout",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2 | |
| "Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1 |