Dror Hilman PhD drorhilman
drorhilman
/ jupyter_echarts.py
Last active
January 17, 2024 17:48
A script to display echarts charts in jupyter notebook, both in notebook and in vscode!
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| # -- consts -- | |
| ECHARTS_CDN = "https://cdn.jsdelivr.net/npm/echarts@5.3.2/dist/echarts.min" | |
| ECHARTS_REQUIREJS_CONF = f"requirejs.config({{paths: {{echarts: '{ECHARTS_CDN}',}}}});" | |
| ECHARTS_TEMPLATE = f""" | |
| <div id="{{ID}}" style="width: {{WIDTH}};height:{{HEIGHT}};"></div> | |
| <script type="module"> | |
| {ECHARTS_REQUIREJS_CONF} | |
| requirejs(["echarts"], (echarts) => {{ | |
| let myChart = echarts.init(document.getElementById('{{ID}}')); |
drorhilman
/ Sqlite Data Frame.ipynb
Created
November 20, 2018 04:22
— forked from seanjtaylor/Sqlite Data Frame.ipynb
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drorhilman
/ datahack2017_4.py
Created
November 18, 2017 07:39
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| from keras.layers import Dense, Dropout, Input, BatchNormalization | |
| from keras.models import Model | |
| def get_nn_model(dropout=0.6): | |
| inp = Input(shape = (90,)) | |
| x = Dense(100, activation='relu')(inp) | |
| x = BatchNormalization()(x) | |
| x = Dropout(dropout)(x) | |
| x = Dense(50, activation='relu')(x) | |
| x = BatchNormalization()(x) |
drorhilman
/ datahack2017_3.py
Created
November 18, 2017 06:35
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| def is_nuc(nuc): | |
| try: | |
| n1, n2 = nuc.split(':') | |
| if not n1 in 'ATGC' and n2 in 'ATGC': | |
| return np.nan | |
| else: | |
| return nuc | |
| except: | |
| return np.nan | |
drorhilman
/ snps_datahack2017_2.py
Created
November 17, 2017 10:16
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| from sklearn.metrics import confusion_matrix | |
| import seaborn as sns | |
| forest.fit(x,y) | |
| predict = forest.predict(x) | |
| cm = pd.DataFrame(confusion_matrix(y, predict), columns=forest.classes_, index=forest.classes_) | |
| sns.heatmap(cm) |
drorhilman
/ snps_datahack2017_1.py
Created
November 17, 2017 10:13
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| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.model_selection import cross_val_score | |
| forest = RandomForestClassifier(n_estimators=30, class_weight='balanced') | |
| x = encoded[[c for c in encoded.columns if 'snp' in c]].values | |
| y = classes.values | |
| scores = cross_val_score(forest, x, y, cv=5) | |
| scores.mean(), scores.std() |