{"id":23169,"date":"2022-11-24T09:02:07","date_gmt":"2022-11-24T03:32:07","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-why-couldnt-i-predict-directly-using-features-matrix\/"},"modified":"2022-11-24T09:02:07","modified_gmt":"2022-11-24T03:32:07","slug":"solved-why-couldnt-i-predict-directly-using-features-matrix","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-why-couldnt-i-predict-directly-using-features-matrix\/","title":{"rendered":"[Solved] Why couldn&#8217;t I predict directly using Features Matrix?"},"content":{"rendered":"<p> [ad_1]<br \/>\n<\/p>\n<div id=\"answer-52098149\" class=\"answer js-answer accepted-answer js-accepted-answer\" data-answerid=\"52098149\" data-parentid=\"52088665\" data-score=\"0\" data-position-on-page=\"1\" data-highest-scored=\"1\" data-question-has-accepted-highest-score=\"1\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n<div class=\"post-layout\">\n<div class=\"votecell post-layout--left\"><\/div>\n<div class=\"answercell post-layout--right\">\n<div class=\"s-prose js-post-body\" itemprop=\"text\">\n<p>You are using this method in both training and testing:<\/p>\n<pre><code>def encode_string(cat_features):\n    enc = preprocessing.LabelEncoder()\n    enc.fit(cat_features)\n    enc_cat_features = enc.transform(cat_features)\n    ohe = preprocessing.OneHotEncoder()\n    encoded = ohe.fit(enc_cat_features.reshape(-1,1))\n    return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()\n<\/code><\/pre>\n<p>by calling:<\/p>\n<pre><code>Features = encode_string(combined_custs['CountryRegionName'])\nfor col in categorical_columns:\n    temp = encode_string(combined_custs[col])\n    Features = np.concatenate([Features, temp],axis=1)\n<\/code><\/pre>\n<p>But as I said in my comment above, you need to apply same preprocessing on the test as you did in train.<\/p>\n<p>Here what happens is, during testing, depending on the order of data in the <code>x_test_data<\/code>, the encoding changes. So maybe a string value which got the number 0, during training is now getting number 1, and the order of features in your final <code>Features<\/code> changes.<\/p>\n<p>To solve this, you need to save the LabelEncoder and OneHotEncoder for each column separately. <\/p>\n<p>So during training, do this:<\/p>\n<pre><code>import pickle\ndef encode_string(cat_features):\n    enc = preprocessing.LabelEncoder()\n    enc.fit(cat_features)\n    enc_cat_features = enc.transform(cat_features)\n\n    # Save the LabelEncoder for this column\n    encoder_file = open('.\/'+cat_features+'_encoder.pickle', 'wb')\n    pickle.dump(lin_mod, encoder_file)\n    encoder_file.close()\n\n    ohe = preprocessing.OneHotEncoder()\n    encoded = ohe.fit(enc_cat_features.reshape(-1,1))\n\n    # Same for OHE\n    ohe_file = open('.\/'+cat_features+'_ohe.pickle', 'wb')\n    pickle.dump(lin_mod, ohe_file)\n    ohe_file.close()\n\n    return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()\n<\/code><\/pre>\n<p>And then, during testing:<\/p>\n<pre><code>def encode_string(cat_features):\n    # Load the previously saved encoder\n    with open('.\/'+cat_features+'_encoder.pickle', 'rb') as file:\n        enc = pickle.load(file)\n\n    # No fitting, only transform\n    enc_cat_features = enc.transform(cat_features)\n\n    # Same for OHE\n    with open('.\/'+cat_features+'_ohe.pickle', 'rb') as file:\n        enc = pickle.load(file)\n\n    return encoded.transform(enc_cat_features.reshape(-1,1)).toarray()\n<\/code><\/pre>\n<\/p><\/div>\n<div class=\"mt24\"><\/div>\n<\/div>\n<p>            <span class=\"d-none\" itemprop=\"commentCount\">1<\/span> <\/p><\/div>\n<\/div>\n<p>[ad_2]<\/p>\n<p>solved Why couldn&#8217;t I predict directly using Features Matrix? <\/p>\n","protected":false},"excerpt":{"rendered":"<p>[ad_1] You are using this method in both training and testing: def encode_string(cat_features): enc = preprocessing.LabelEncoder() enc.fit(cat_features) enc_cat_features = enc.transform(cat_features) ohe = preprocessing.OneHotEncoder() encoded = ohe.fit(enc_cat_features.reshape(-1,1)) return encoded.transform(enc_cat_features.reshape(-1,1)).toarray() by calling: Features = encode_string(combined_custs[&#8216;CountryRegionName&#8217;]) for col in categorical_columns: temp = encode_string(combined_custs[col]) Features = np.concatenate([Features, temp],axis=1) But as I said in my comment above, you need to &#8230; <a title=\"[Solved] Why couldn&#8217;t I predict directly using Features Matrix?\" class=\"read-more\" href=\"https:\/\/jassweb.com\/solved\/solved-why-couldnt-i-predict-directly-using-features-matrix\/\" aria-label=\"More on [Solved] Why couldn&#8217;t I predict directly using Features Matrix?\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[5014,1173,792],"class_list":["post-23169","post","type-post","status-publish","format-standard","hentry","category-solved","tag-edx","tag-machine-learning","tag-scikit-learn"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>[Solved] Why couldn&#039;t I predict directly using Features Matrix? - JassWeb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jassweb.com\/solved\/solved-why-couldnt-i-predict-directly-using-features-matrix\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[Solved] Why couldn&#039;t I predict directly using Features Matrix? - JassWeb\" \/>\n<meta property=\"og:description\" content=\"[ad_1] You are using this method in both training and testing: def encode_string(cat_features): enc = preprocessing.LabelEncoder() enc.fit(cat_features) enc_cat_features = enc.transform(cat_features) ohe = preprocessing.OneHotEncoder() encoded = ohe.fit(enc_cat_features.reshape(-1,1)) return encoded.transform(enc_cat_features.reshape(-1,1)).toarray() by calling: Features = encode_string(combined_custs[&#039;CountryRegionName&#039;]) for col in categorical_columns: temp = encode_string(combined_custs[col]) Features = np.concatenate([Features, temp],axis=1) But as I said in my comment above, you need to ... 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