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{"lastUpload":"2020-09-13T21:20:27.543Z","extensionVersion":"v3.4.3"}

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bind c "use weapon_knife;use weapon_flashbang";

bind f "use weapon_knife;use weapon_smokegrenade";

bind "v" "use weapon_knife;use weapon_molotov; use weapon_incgrenade"

bind capslock "use weapon_knife;use weapon_hegrenade";

bind z "+lookatweapon";

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[
  {
    "metadata": {
      "id": "70378119-1d85-4935-9733-0298c7a369a2",
      "publisherId": "steoates.autoimport",
      "publisherDisplayName": "steoates"
    },
    "name": "autoimport",
    "publisher": "steoates",
    "version": "1.5.3"

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from sklearn.svm import LinearSVC
clf = LinearSVC(C=10, random_state=0)
clf.fit(X_train, y_train)

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from sklearn.feature_extraction.text import TfidfVectorizer
vect = TfidfVectorizer(ngram_range=(1,4), use_idf=True, lowercase=True, min_df=2, max_df=0.95)
vect.fit(df.text_pt)
text_vect = vect.transform(df.text_pt)

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from sklearn.metrics import f1_score
y_prediction = clf.predict(X_test)

f1 = f1_score(y_prediction, y_test, average='weighted')

print(f1)

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from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0, solver='newton-cg')
clf = clf.fit(X_train, y_train)

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from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test = train_test_split(
    text_vect, 
    df.sentiment,
    test_size = 0.3, 
    random_state = 42
)

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from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer(ngram_range=(1, 1))
vect.fit(df.text_pt)
text_vect = vect.transform(df.text_pt)

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df.sentiment = df['sentiment'].map({'pos': 1, 'neg': 0})
df.head()