$ pip install "tensorflow>=1.7.0"

$ git clone https://github.com/tensorflow/adanet && cd adanet

$ cd adanet
$ bazel test -c opt //...

import adanet

pip?install?tpot?

?1from?tpot?import?TPOTClassifier??2from?sklearn.datasets?import?load_iris??3from?sklearn.model_selection?import?train_test_split??4??5#?Load?iris?dataset??6iris?=?load_iris()??7??8#?Split?the?data??9?10X_trainX_train,?X_test,?y_train,?y_test?=?train_test_split(iris.data,?iris.target,?11?train_size=0.75,?test_size=0.25)?12?13#?Fit?the?TPOT?classifier??14?15tpot?=?TPOTClassifier(verbosity=2,?max_time_mins=2)?16tpot.fit(X_train,?y_train)?17?18#?Export?the?pipeline?19tpot.export('tpot_iris_pipeline.py')?

?1import?numpy?as?np??2from?sklearn.kernel_approximation?import?RBFSampler??3from?sklearn.model_selection?import?train_test_split??4from?sklearn.pipeline?import?make_pipeline??5from?sklearn.tree?import?DecisionTreeClassifier??6#?NOTE:?Make?sure?that?the?class?is?labeled?'class'?in?the?data?file??7tpot_data?=?np.recfromcsv('PATH/TO/DATA/FILE',?delimiter='COLUMN_SEPARATOR',?dtype=np.float64)??8features?=?np.delete(tpot_data.view(np.float64).reshape(tpot_data.size,?-1),?tpot_data.dtype.names.index('class'),?axis=1)??9training_features,?testing_features,?training_classes,?testing_classes?=??10?train_test_split(features,?tpot_data['class'],?random_state=42)?11exported_pipeline?=?make_pipeline(?12?RBFSampler(gamma=0.8500000000000001),?13?DecisionTreeClassifier(criterion="entropy",?max_depth=3,?min_samples_leaf=4,?min_samples_split=9)?14)?15exported_pipeline.fit(training_features,?training_classes)?16results?=?exported_pipeline.predict(testing_features)?

1from?tpot?import?TPOTClassifier??2from?sklearn.datasets?import?load_digits??3from?sklearn.model_selection?import?train_test_split??4??5#?load?and?split?dataset???6digitsdigits?==?load_digitsload_di?()??7X_train,?X_test,?y_train,?y_test?=?train_test_split(digits.data,?digits.target,??8?train_size=0.75,?test_size=0.25)??9?10#?Fit?the?TPOT?classifier??11tpot?=?TPOTClassifier(verbosity=2,?max_time_mins=5,?population_size=40)?12tpot.fit(X_train,?y_train)?13?14#?Export?pipeline?15tpot.export('tpot_mnist_pipeline.py')?

?1import?numpy?as?np??2from?sklearn.model_selection?import?train_test_split??3from?sklearn.neighbors?import?KNeighborsClassifier??4#?NOTE:?Make?sure?that?the?class?is?labeled?'class'?in?the?data?file??5tpot_data?=?np.recfromcsv('PATH/TO/DATA/FILE',?delimiter='COLUMN_SEPARATOR',?dtype=np.float64)??6features?=?np.delete(tpot_data.view(np.float64).reshape(tpot_data.size,?-1),?tpot_data.dtype.names.index('class'),?axis=1)??7training_features,?testing_features,?training_classes,?testing_classes?=???8?train_test_split(features,?tpot_data['class'],?random_state=42)??9exported_pipeline?=?KNeighborsClassifier(n_neighbors=4,?p=2,?weights="distance")?10exported_pipeline.fit(training_features,?training_classes)?11results?=?exported_pipeline.predict(testing_features)?

pip?install?shap?

conda?install?-c?conda-forge?shap?

#?this?is?the?code?from?https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py?from?__future__?import?print_function?import?keras?from?keras.datasets?import?mnist?from?keras.models?import?Sequential?from?keras.layers?import?Dense,?Dropout,?Flatten?from?keras.layers?import?Conv2D,?MaxPooling2D?from?keras?import?backend?as?K?batch_size?=?128?num_classes?=?10?epochs?=?12?#?input?image?dimensions?img_rows,?img_cols?=?28,?28?#?the?data,?split?between?train?and?test?sets?(x_train,?y_train),?(x_test,?y_test)?=?mnist.load_data()?if?K.image_data_format()?==?'channels_first':??x_train?=?x_train.reshape(x_train.shape[0],?1,?img_rows,?img_cols)??x_test?=?x_test.reshape(x_test.shape[0],?1,?img_rows,?img_cols)??input_shape?=?(1,?img_rows,?img_cols)?else:??x_train?=?x_train.reshape(x_train.shape[0],?img_rows,?img_cols,?1)??x_test?=?x_test.reshape(x_test.shape[0],?img_rows,?img_cols,?1)??input_shape?=?(img_rows,?img_cols,?1)?x_train?=?x_train.astype('float32')?x_test?=?x_test.astype('float32')?x_train?/=?255?x_test?/=?255?print('x_train?shape:',?x_train.shape)?print(x_train.shape[0],?'train?samples')?print(x_test.shape[0],?'test?samples')?#?convert?class?vectors?to?binary?class?matrices?y_train?=?keras.utils.to_categorical(y_train,?num_classes)?y_test?=?keras.utils.to_categorical(y_test,?num_classes)?model?=?Sequential()?model.add(Conv2D(32,?kernel_size=(3,?3),??activation='relu',??input_shape=input_shape))?model.add(Conv2D(64,?(3,?3),?activation='relu'))?model.add(MaxPooling2D(pool_size=(2,?2)))?model.add(Dropout(0.25))?model.add(Flatten())?model.add(Dense(128,?activation='relu'))?model.add(Dropout(0.5))?model.add(Dense(num_classes,?activation='softmax'))?model.compile(loss=keras.losses.categorical_crossentropy,??optimizer=keras.optimizers.Adadelta(),??metrics=['accuracy'])?model.fit(x_train,?y_train,??batch_size=batch_size,??epochs=epochs,??verbose=1,??validation_data=(x_test,?y_test))?score?=?model.evaluate(x_test,?y_test,?verbose=0)?print('Test?loss:',?score[0])?print('Test?accuracy:',?score[1])?

pip?install?optimuspyspark?

from?optimus?import?Optimus?op?=?Optimus()?#?This?is?a?custom?function?def?func(value,?arg):??return?"this?was?a?number"?df?=op.load.url("https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/foo.csv")?df??.rows.sort("product","desc")??.cols.lower(["firstName","lastName"])??.cols.date_transform("birth",?"new_date",?"yyyy/MM/dd",?"dd-MM-YYYY")??.cols.years_between("birth",?"years_between",?"yyyy/MM/dd")??.cols.remove_accents("lastName")??.cols.remove_special_chars("lastName")??.cols.replace("product","taaaccoo","taco")??.cols.replace("product",["piza","pizzza"],"pizza")??.rows.drop(df["id"]<7)??.cols.drop("dummyCol")??.cols.rename(str.lower)??.cols.apply_by_dtypes("product",func,"string",?data_type="integer")??.cols.trim("*")??.show()?

pip3?install?spacy?$?python3?-m?spacy?download?en?

#?python?-m?spacy?download?en_core_web_sm?import?spacy?#?Load?English?tokenizer,?tagger,?parser,?NER?and?word?vectors?nlp?=?spacy.load('en_core_web_sm')?#?Process?whole?documents?text?=?(u"When?Sebastian?Thrun?started?working?on?self-driving?cars?at?"??u"Google?in?2007,?few?people?outside?of?the?company?took?him?"??u"seriously.?¡°I?can?tell?you?very?senior?CEOs?of?major?American?"??u"car?companies?would?shake?my?hand?and?turn?away?because?I?wasn¡¯t?"??u"worth?talking?to,¡±?said?Thrun,?now?the?co-founder?and?CEO?of?"??u"online?higher?education?startup?Udacity,?in?an?interview?with?"??u"Recode?earlier?this?week.")?doc?=?nlp(text)?#?Find?named?entities,?phrases?and?concepts?for?entity?in?doc.ents:??print(entity.text,?entity.label_)?#?Determine?semantic?similarities?doc1?=?nlp(u"my?fries?were?super?gross")?doc2?=?nlp(u"such?disgusting?fries")?similarity?=?doc1.similarity(doc2)?print(doc1.text,?doc2.text,?similarity)?

Sebastian?Thrun?PERSON?Google?ORG?2007?DATE?American?NORP?Thrun?PERSON?Recode?ORG?earlier?this?week?DATE?my?fries?were?super?gross?such?disgusting?fries?0.7139701635071919?

pip?install?jupytext?--upgrade?

c.NotebookApp.contents_manager_class?=?"jupytext.TextFileContentsManager"?

jupyter?notebook?

pip3?install?chartify?

import?pandas?as?pd?import?chartify?#?Generate?example?data?data?=?chartify.examples.example_data()?

total_quantity_by_month_and_fruit?=?(data.groupby(??[data['date']?+?pd.offsets.MonthBegin(-1),?'fruit'])['quantity'].sum()??.reset_index().rename(columns={'date':?'month'})??.sort_values('month'))?print(total_quantity_by_month_and_fruit.head())?month?fruit?quantity?0?2017-01-01?Apple?7?1?2017-01-01?Banana?6?2?2017-01-01?Grape?1?3?2017-01-01?Orange?2?4?2017-02-01?Apple?8?

#?Plot?the?data?ch?=?chartify.Chart(blank_labels=True,?x_axis_type='datetime')?ch.set_title("Stacked?area")?ch.set_subtitle("Represent?changes?in?distribution.")?ch.plot.area(??data_frame=total_quantity_by_month_and_fruit,??x_column='month',??y_column='quantity',??color_column='fruit',??stacked=True)?ch.show('png')?