markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
List the model files uploaded during the run: | print(best_run.get_file_names()) | _____no_output_____ | Apache-2.0 | azure/notebooks/.ipynb_checkpoints/HPO-RAPIDS-checkpoint.ipynb | miguelusque/cloud-ml-examples |
Register the folder (and all files in it) as a model named `train-rapids` under the workspace for deployment | # model = best_run.register_model(model_name='train-rapids', model_path='outputs/model-rapids.joblib') | _____no_output_____ | Apache-2.0 | azure/notebooks/.ipynb_checkpoints/HPO-RAPIDS-checkpoint.ipynb | miguelusque/cloud-ml-examples |
Delete cluster | # delete the cluster
# gpu_cluster.delete() | _____no_output_____ | Apache-2.0 | azure/notebooks/.ipynb_checkpoints/HPO-RAPIDS-checkpoint.ipynb | miguelusque/cloud-ml-examples |
[Optional]: If you're using a Mac/Linux, you can check your environment with these commands:```!which pip3!which python3!ls -lah /usr/local/bin/python3``` | !pip3 install -U pip
!pip3 install torch==1.3.0
!pip3 install seaborn
import torch
torch.cuda.is_available()
# IPython candies...
from IPython.display import Image
from IPython.core.display import HTML
from IPython.display import clear_output
%%html
<style> table {float:left} </style> | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Perceptron=====**Perceptron** algorithm is a:> "*system that depends on **probabilistic** rather than deterministic principles for its operation, gains its reliability from the **properties of statistical measurements obtain from a large population of elements***"> \- Frank Rosenblatt (1957)Then the news:> "*[Perceptro... | ##Image(url="perceptron.png", width=500)
Image(url="https://ibin.co/4TyMU8AdpV4J.png", width=500) | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
(**Note:** Usually, we use $x_1$ as the bias and fix the input to 1) Perceptron as a Workflow Diagram----If you're familiar with [mermaid flowchart](https://mermaidjs.github.io)```.. mermaid:: graph LR subgraph Input x_1 x_i x_n end subgraph Perceptron n1((s))... | ##Image(url="perceptron-mermaid.svg", width=500)
Image(url="https://svgshare.com/i/AbJ.svg", width=500) | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Optimization Process====To learn the weights, $w$, we use an **optimizer** to find the best-fit (optimal) values for $w$ such that the inputs correct maps to the outputs.Typically, process performs the following 4 steps iteratively. **Initialization** - **Step 1**: Initialize weights vector **Forward Propagation** - ... | import math
import numpy as np
np.random.seed(0)
def sigmoid(x): # Returns values that sums to one.
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(sx):
# See https://math.stackexchange.com/a/1225116
# Hint: let sx = sigmoid(x)
return sx * (1 - sx)
sigmoid(np.array([2.5, 0.32, -1.42])) ... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Representing OR Boolean---Lets consider the problem of the OR boolean and apply the perceptron with simple gradient descent. | x2 | x3 | y | |:--:|:--:|:--:|| 0 | 0 | 0 || 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 1 | | X = or_input = np.array([[0,0], [0,1], [1,0], [1,1]])
Y = or_output = np.array([[0,1,1,1]]).T
or_input
or_output
# Define the shape of the weight vector.
num_data, input_dim = or_input.shape
# Define the shape of the output vector.
output_dim = len(or_output.T)
print('Inputs\n======')
print('no. of rows =', num_data) ... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Step 2a: Multiply the weights vector with the inputs, sum the products====To get the output of step 2a, - Itrate through each row of the data, `X` - For each column in each row, find the product of the value and the respective weights - For each row, compute the sum of the products | # If we write it imperatively:
summation = []
for row in X:
sum_wx = 0
for feature, weight in zip(row, W):
sum_wx += feature * weight
summation.append(sum_wx)
print(np.array(summation))
# If we vectorize the process and use numpy.
np.dot(X, W) | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Train the Single-Layer Model==== | num_epochs = 10000 # No. of times to iterate.
learning_rate = 0.03 # How large a step to take per iteration.
# Lets standardize and call our inputs X and outputs Y
X = or_input
Y = or_output
for _ in range(num_epochs):
layer0 = X
# Step 2a: Multiply the weights vector with the inputs, sum the products, i.e. ... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Lets try the XOR Boolean---Lets consider the problem of the OR boolean and apply the perceptron with simple gradient descent. | x2 | x3 | y | |:--:|:--:|:--:|| 0 | 0 | 0 || 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 0 | | X = xor_input = np.array([[0,0], [0,1], [1,0], [1,1]])
Y = xor_output = np.array([[0,1,1,0]]).T
xor_input
xor_output
num_epochs = 10000 # No. of times to iterate.
learning_rate = 0.003 # How large a step to take per iteration.
# Lets drop the last row of data and use that as unseen test.
X = xor_input
Y = xor_output
... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
You can't represent XOR with simple perceptron !!!====No matter how you change the hyperparameters or data, the XOR function can't be represented by a single perceptron layer. There's no way you can get all four data points to get the correct outputs for the XOR boolean operation. Solving XOR (Add more layers)==== | from itertools import chain
import numpy as np
np.random.seed(0)
def sigmoid(x): # Returns values that sums to one.
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(sx):
# See https://math.stackexchange.com/a/1225116
return sx * (1 - sx)
# Cost functions.
def cost(predicted, truth):
return truth - ... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Now try adding another layer====Use the same process: 1. Initialize 2. Forward Propagate 3. Back Propagate 4. Update (aka step) | from itertools import chain
import numpy as np
np.random.seed(0)
def sigmoid(x): # Returns values that sums to one.
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(sx):
# See https://math.stackexchange.com/a/1225116
return sx * (1 - sx)
# Cost functions.
def cost(predicted, truth):
return truth - ... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Now, lets do it with PyTorch First lets try a single perceptron and see that we can't train a model that can represent XOR. | from tqdm import tqdm
import torch
from torch import nn
from torch.autograd import Variable
from torch import FloatTensor
from torch import optim
use_cuda = torch.cuda.is_available()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("darkgrid")
sns.set(rc={'figure.figsize':(15, 10... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
(**Note**: Personally, I strongely encourage you to go through the [University of Washington course of machine learning regression](https://www.coursera.org/learn/ml-regression) to better understand the fundamentals of (i) ***gradient***, (ii) ***loss*** and (iii) ***optimizer***. But given that you know how to code it... | num_epochs = 1000
# Step 1: Initialization.
# Note: When using PyTorch a lot of the manual weights
# initialization is done automatically when we define
# the model (aka architecture)
model = nn.Sequential(
nn.Linear(input_dim, output_dim),
nn.Sigmoid())
criterion = nn.MSELoss()
l... | Input: [0, 0]
Pred: 0
Ouput: 0
######
Input: [0, 1]
Pred: 0
Ouput: 1
######
Input: [1, 0]
Pred: 0
Ouput: 1
######
Input: [1, 1]
Pred: 0
Ouput: 0
######
| MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Now, try again with 2 layers using PyTorch==== | %%time
hidden_dim = 5
num_data, input_dim = X_pt.shape
num_data, output_dim = Y_pt.shape
model = nn.Sequential(nn.Linear(input_dim, hidden_dim),
nn.Sigmoid(),
nn.Linear(hidden_dim, output_dim),
nn.Sigmoid())
criterion = nn.MSELoss()
learning_rate = 0... | Input: [0, 0]
Pred: 0
Ouput: 0
######
Input: [0, 1]
Pred: 1
Ouput: 1
######
Input: [1, 0]
Pred: 1
Ouput: 1
######
Input: [1, 1]
Pred: 0
Ouput: 0
######
| MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
MNIST: The "Hello World" of Neural Nets====Like any deep learning class, we ***must*** do the MNIST. The MNIST dataset is - is made up of handwritten digits - 60,000 examples training set - 10,000 examples test set | # We're going to install tensorflow here because their dataset access is simpler =)
!pip3 install torchvision
from torchvision import datasets, transforms
mnist_train = datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
tran... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Lets apply what we learn about multi-layered perceptron with PyTorch and apply it to the MNIST data. | X_mnist = mnist_train.data.float()
Y_mnist = mnist_train.targets.float()
X_mnist_test = mnist_test.data.float()
Y_mnist_test = mnist_test.targets.float()
Y_mnist.shape
# Use FloatTensor.shape to get the shape of the matrix/tensor.
num_data, *input_dim = X_mnist.shape
print('No. of images:', num_data)
print('Inputs Dim... | _____no_output_____ | MIT | completed/Session 1 - Hello DL World.ipynb | tongni1975/tsundoku |
Interpolation | df = pd.DataFrame(
{
'name': ['A','A', 'B','B','B','B', 'C','C','C'],
'value': [1, np.nan, np.nan, 2, 3, 1, 3, np.nan, 3],
}
)
df
df.fillna(method="pad")
df['value'].interpolate()
df['value'].interpolate(method="quadratic")
# For cumsums
df['value'].interpolate(method="pchip") | _____no_output_____ | Apache-2.0 | Time Series/Missing-values_Multistep_Multivariate.ipynb | AlexKH22/Finance |
LSTM | from pandas import read_csv
from datetime import datetime
# load data
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('data/raw_pollution.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)
# manually specify c... | (8760, 1, 8) (8760,) (35039, 1, 8) (35039,)
| Apache-2.0 | Time Series/Missing-values_Multistep_Multivariate.ipynb | AlexKH22/Finance |
We will define the LSTM with 50 neurons in the first hidden layer and 1 neuron in the output layer for predicting pollution. The input shape will be 1 time step with 8 features.We will use the Mean Absolute Error (MAE) loss function and the efficient Adam version of stochastic gradient descent. | # design network
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X, train_y, epochs=50, batch_size=64,
validation_data=(test_X, test_y), verbose=2, shuf... | _____no_output_____ | Apache-2.0 | Time Series/Missing-values_Multistep_Multivariate.ipynb | AlexKH22/Finance |
N previous lags | # load dataset
dataset = read_csv('data/pollution.csv', header=0, index_col=0)
values = dataset.values
# integer encode direction
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_rang... | (43797, 32)
| Apache-2.0 | Time Series/Missing-values_Multistep_Multivariate.ipynb | AlexKH22/Finance |
Next, we need to be more careful in specifying the column for input and output.We have 3 * 8 + 8 columns in our framed dataset. We will take 3 * 8 or 24 columns as input for the obs of all features across the previous 3 hours. We will take just the pollution variable as output at the following hour, as follows: | # split into train and test sets
values = reframed.values
n_train_hours = 365 * 24
train = values[:n_train_hours]
test = values[n_train_hours:]
# split into input and outputs
n_obs = n_hours * n_features
train_X, train_y = train[:, :n_obs], train[:, -n_features]
test_X, test_y = test[:, :n_obs], test[:, -n_features]
p... | Test RMSE: 22.785
| Apache-2.0 | Time Series/Missing-values_Multistep_Multivariate.ipynb | AlexKH22/Finance |
Notebook for creating complete HRDPS file for 2007 from 2007 data, for every variable, all at once. For a more polished notebook, see generalPredictions4c. This notebook has been superceded by that notebook. Importing Training Data | Description | HRDPS | CANRCM | | ----------- | ----------- | ----------- |... | variables = [['tair', 'tas', 'Near-Surface Air Temperature'],
['precip', 'pr', 'Precipitation'],
['atmpres', 'psl', 'Sea Level Pressure'],
['qair', 'huss', 'Near Surface Specific Humidity'],
['solar', 'rsds', 'Shortwave radiation'],
['therm_rad', 'rl... | _____no_output_____ | Apache-2.0 | notebooks/generalPredictions4b.ipynb | UBC-MOAD/analysis-armaan |
PCA Functions | ##transforms and concatenates two data sets
def transform2(data1, data2):
A_mat = transform(data1)
B_mat = transform(data2)
return np.concatenate((A_mat, B_mat), axis=0)
##inverse function of transform2 - splits data matrix and returns two data sets
def reverse2(matrix, orig_shape):
split4 = int( matri... | _____no_output_____ | Apache-2.0 | notebooks/generalPredictions4b.ipynb | UBC-MOAD/analysis-armaan |
Multiple Linear Regression Functions | ##functions that use multiple linear regression to fit eigenvectors
##takes CANRCM eigenvectors (x1, x2, x3, x4...) and HRDPS eigenvectors (y1, y2, y3...)
##For each y from 0:result_size, approximates yn = a0 + a1*x1 + a2*x2 + a3*x3 ... using num_vec x's
##getCoefs returns (coeficients, intercept)
##fit_modes returns e... | _____no_output_____ | Apache-2.0 | notebooks/generalPredictions4b.ipynb | UBC-MOAD/analysis-armaan |
Projection Function | ##scalar projection of u onto v - with extra 1/norm factor (for math reasons)
##projectData projects the data onto each principle conponent, at each time
##output is a set of eigenvectors
def project(u, v):
v_norm = np.sqrt(np.sum(v**2))
return np.dot(u, v)/v_norm**2
def projectData(data_mat, new_PCs, n... | _____no_output_____ | Apache-2.0 | notebooks/generalPredictions4b.ipynb | UBC-MOAD/analysis-armaan |
Overall Function | def reconstruct(downscale_mat, mean, can_PCs, can_me, hr_PCs, hr_me, n, r, method = 'LS', EB = 'False'):
coefs = getCoefs(can_me, n + 1, hr_me, r + 1, type = method)
proj = np.concatenate((mean.reshape(1, -1), projectData(downscale_mat - mean, can_PCs, n)), axis = 0)
pred_eigs = np.matmul(coefs[0], proj) +... | _____no_output_____ | Apache-2.0 | notebooks/generalPredictions4b.ipynb | UBC-MOAD/analysis-armaan |
Reconstructing Data | data = ()
##reconstructing u and v winds
import_wind_data()
##PCA on CANRCM 2007
can07_mat = transform2(can07_u, can07_v)
can07_PCs, can07_eigs, can07_mean = doPCA(100, can07_mat)
##PCA on HRDPS 2007
hr07_mat = transform2(hr07_u, hr07_v)
hr07_PCs, hr07_eigs, hr07_mean = doPCA(100, hr07_mat)
## combining the eigenve... | _____no_output_____ | Apache-2.0 | notebooks/generalPredictions4b.ipynb | UBC-MOAD/analysis-armaan |
DBSCAN without Libraries | import time
import warnings
import queue
import numpy as np
import pandas as pd
from sklearn import cluster, datasets, mixture
from sklearn.neighbors import kneighbors_graph
from sklearn import datasets
# from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from scipy.stats import... | /Users/kiat/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/extmath.py:770: RuntimeWarning: invalid value encountered in true_divide
updated_mean = (last_sum + new_sum) / updated_sample_count
/Users/kiat/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/extmath.py:711: RuntimeWarning: Degrees of freedom... | MIT | Code-Example-023-DBSCAN.ipynb | waltsco059/Elements-of-Data-Analytics |
Copyright 2019 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Transfer Learning Using Pretrained ConvNets Run in Google Colab View source on GitHub | from __future__ import absolute_import, division, print_function
import tensorflow.compat.v2 as tf #nightly-gpu
tf.enable_v2_behavior()
import os
import numpy as np
import matplotlib.pyplot as plt
from functools import partial
tf.__version__ | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Data preprocessing Data download | _URL = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
zip_file = tf.keras.utils.get_file(origin=_URL,
fname="flower_photos.tgz",
extract=True)
base_dir = os.path.join(os.path.dirname(zip_file), 'flower_p... | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Create the base model from the pre-trained convnets | IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)
# Create the base model from the pre-trained model MobileNet V2
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet') | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Feature extractionYou will freeze the convolutional base created from the previous step and use that as a feature extractor, add a classifier on top of it and train the top-level classifier. | base_model.trainable = False | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Add a classification head | model = tf.keras.Sequential([
base_model,
tf.keras.layers.Conv2D(32, 3, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(5, activation='softmax')
]) | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Compile the modelYou must compile the model before training it. Since there are two classes, use a binary cross-entropy loss. | model.compile(optimizer=tf.keras.optimizers.Adam(),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary() | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
The 2.5M parameters in MobileNet are frozen, but there are 1.2K _trainable_ parameters in the Dense layer. These are divided between two `tf.Variable` objects, the weights and biases. | len(model.trainable_variables)
train_generator.labels | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Train the model | epochs = 10
history = model.fit(train_generator,
epochs=epochs,
validation_data=val_generator) | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Learning curvesLet's take a look at the learning curves of the training and validation accuracy/loss when using the MobileNet V2 base model as a fixed feature extractor. | acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.yl... | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Note: If you are wondering why the validation metrics are clearly better than the training metrics, the main factor is because layers like `tf.keras.layers.BatchNormalization` and `tf.keras.layers.Dropout` affect accuracy during training. They are turned off when calculating validation loss.To a lesser extent, it is al... | base_model.trainable = True
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))
# Fine tune from this layer onwards
fine_tune_at = 100
# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at... | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Compile the modelCompile the model using a much lower training rate. | model.compile(loss='categorical_crossentropy',
optimizer = tf.keras.optimizers.Adam(1e-5),
metrics=['accuracy'])
model.summary()
len(model.trainable_variables) | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Continue Train the model If you trained to convergence earlier, this will get you a few percent more accuracy. | history_fine = model.fit(train_generator,
epochs=5,
validation_data=val_generator) | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Let's take a look at the learning curves of the training and validation accuracy/loss, when fine tuning the last few layers of the MobileNet V2 base model and training the classifier on top of it. The validation loss is much higher than the training loss, so you may get some overfitting.You may also get some overfittin... | saved_model_dir = 'save/fine_tuning'
tf.saved_model.save(model, saved_model_dir)
model = tf.saved_model.load(saved_model_dir)
concrete_func = model.signatures[
tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
concrete_func.inputs[0].set_shape([1, 224, 224, 3])
converter = tf.lite.TFLiteConverter.from_concrete_fun... | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
Download the converted model | from google.colab import files
files.download('model.tflite')
acc += history_fine.history['accuracy']
val_acc += history_fine.history['val_accuracy']
loss += history_fine.history['loss']
val_loss += history_fine.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy... | _____no_output_____ | Apache-2.0 | community/en/tensorflow_for_poets_reboot.ipynb | surabhithakare2128/examples |
 [](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb) Spark NLP Basics and P... | import os
# Install java
! apt-get update -qq
! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
os.environ["PATH"] = os.environ["JAVA_HOME"] + "/bin:" + os.environ["PATH"]
! java -version
# Install pyspark
! pip install --ignore-installed -q pys... | openjdk version "1.8.0_275"
OpenJDK Runtime Environment (build 1.8.0_275-8u275-b01-0ubuntu1~18.04-b01)
OpenJDK 64-Bit Server VM (build 25.275-b01, mixed mode)
[K |████████████████████████████████| 215.7MB 70kB/s
[K |████████████████████████████████| 204kB 48.3MB/s
[?25h Building wheel for pyspark (setup.p... | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
**How to prevent Google Colab from disconnecting?**Google Colab notebooks have an idle timeout of 90 minutes and absolute timeout of 12 hours. This means, if user does not interact with his Google Colab notebook for more than 90 minutes, its instance is automatically terminated. Also, maximum lifetime of a Colab insta... | '''
function ConnectButton(){
console.log("Connect pushed");
document.querySelector("#top-toolbar > colab-connect-button").shadowRoot.querySelector("#connect").click()
}
setInterval(ConnectButton,60000);
''' | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
1. Start Spark Session | import sparknlp
spark = sparknlp.start()
# params =>> gpu=False, spark23=False (start with spark 2.3)
print("Spark NLP version", sparknlp.version())
print("Apache Spark version:", spark.version)
! cd ~/.ivy2/cache/com.johnsnowlabs.nlp/spark-nlp_2.11/jars && ls -lt | total 39960
-rw-r--r-- 1 root root 40918604 Jan 8 21:03 spark-nlp_2.11-2.7.1.jar
| Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
if you want to work with Spark 2.3 ```import os Install java! apt-get update -qq! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null!wget -q https://archive.apache.org/dist/spark/spark-2.3.0/spark-2.3.0-bin-hadoop2.7.tgz!tar xf spark-2.3.0-bin-hadoop2.7.tgz!pip install -q findsparkos.environ["JAVA_HOME"] = "/u... | # https://github.com/JohnSnowLabs/spark-nlp/blob/master/python/sparknlp/__init__.py
from pyspark.sql import SparkSession
def start(gpu=False, spark23=False):
current_version="2.5.4"
maven_spark24 = "com.johnsnowlabs.nlp:spark-nlp_2.11:{}".format(current_version)
maven_gpu_spark24 = "com.johnsnowlabs.nlp:s... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
If you want to start `SparkSession` with your own parameters or you need to load the required jars/packages from your local disk, or you have no internet connection (that would be needed to pull the required packages from internet), you can skip `sparknlp.start()` and start your session manually as shown below. | """
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Spark NLP Enterprise 2.4.5") \
.master("local[8]") \
.config("spark.driver.memory","12G") \
.config("spark.driver.maxResultSize", "2G") \
.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \
... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
CPU on Apache Spark 2.4.x: https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/spark-nlp-assembly-2.7.1.jarGPU on Apache Spark 2.4.x: https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/spark-nlp-gpu-assembly-2.7.1.jarCPU on Apache Spark 2.3.x: https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/spark-nl... | from sparknlp.pretrained import PretrainedPipeline
testDoc = '''
Peter is a very good persn.
My life in Russia is very intersting.
John and Peter are brothrs. However they don't support each other that much.
Lucas Nogal Dunbercker is no longer happy. He has a good car though.
Europe is very culture rich. There are huge... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Explain Document ML **Stages**- DocumentAssembler- SentenceDetector- Tokenizer- Lemmatizer- Stemmer- Part of Speech- SpellChecker (Norvig) | pipeline = PretrainedPipeline('explain_document_ml', lang='en')
pipeline.model.stages
# Load pretrained pipeline from local disk:
pipeline_local = PretrainedPipeline.from_disk('/root/cache_pretrained/explain_document_ml_en_2.4.0_2.4_1580252705962')
%%time
result = pipeline.annotate(testDoc)
result.keys()
result['sen... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Explain Document DL **Stages**- DocumentAssembler- SentenceDetector- Tokenizer- NER (NER with GloVe 100D embeddings, CoNLL2003 dataset)- Lemmatizer- Stemmer- Part of Speech- SpellChecker (Norvig) | pipeline_dl = PretrainedPipeline('explain_document_dl', lang='en')
pipeline_dl.model.stages
pipeline_dl.model.stages[-2].getStorageRef()
pipeline_dl.model.stages[-2].getClasses()
%%time
result = pipeline_dl.annotate(testDoc)
result.keys()
result.keys()
result['entities']
df = pd.DataFrame({'token':result['token'], '... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Recognize Entities DL | recognize_entities = PretrainedPipeline('recognize_entities_dl', lang='en')
recognize_entities.model.stages
recognize_entities.model.stages[3].getStorageRef()
recognize_entities.model.stages[4].getClasses()
testDoc = '''
Peter is a very good persn.
My life in Russia is very intersting.
John and Peter are brothrs. Howe... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Clean Stop Words | clean_stop = PretrainedPipeline('clean_stop', lang='en')
result = clean_stop.annotate(testDoc)
result.keys()
' '.join(result['cleanTokens']) | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Clean Slang | clean_slang = PretrainedPipeline('clean_slang', lang='en')
result = clean_slang.annotate(' Whatsup bro, call me ASAP')
' '.join(result['normal'])
clean_slang.model.stages
clean_slang.model.stages[-1] | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Spell Checker (Norvig Algo)ref: https://norvig.com/spell-correct.html | spell_checker = PretrainedPipeline('check_spelling', lang='en')
testDoc = '''
Peter is a very good persn.
My life in Russia is very intersting.
John and Peter are brothrs. However they don't support each other that much.
Lucas Nogal Dunbercker is no longer happy. He has a good car though.
Europe is very culture rich. ... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Spell Checker DLhttps://medium.com/spark-nlp/applying-context-aware-spell-checking-in-spark-nlp-3c29c46963bc | spell_checker_dl = PretrainedPipeline('check_spelling_dl', lang='en')
text = 'We will go to swimming if the ueather is nice.'
result = spell_checker_dl.annotate(text)
list(zip(result['token'], result['checked']))
result.keys()
# check for the different occurrences of the word "ueather"
examples = ['We will go to swi... | [('We', 'We'), ('will', 'will'), ('go', 'go'), ('to', 'to'), ('swimming', 'swimming'), ('if', 'if'), ('the', 'the'), ('ueather', 'weather'), ('is', 'is'), ('nice', 'nice'), ('.', '.')]
[('I', 'I'), ('have', 'have'), ('a', 'a'), ('black', 'black'), ('ueather', 'weather'), ('jacket', 'jacket'), (',', ','), ('so', 'so'), ... | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Parsing a list of texts | testDoc_list = ['French author who helped pioner the science-fiction genre.',
'Verne wrate about space, air, and underwater travel before navigable aircrast',
'Practical submarines were invented, and before any means of space travel had been devised.']
testDoc_list
pipeline = PretrainedPipeline('explain_document_ml', ... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Using fullAnnotate to get more details ```annotatorType: String, begin: Int, end: Int, result: String, (this is what annotate returns)metadata: Map[String, String], embeddings: Array[Float]``` | text = 'Peter Parker is a nice guy and lives in New York'
# pipeline_dl >> explain_document_dl
detailed_result = pipeline_dl.fullAnnotate(text)
detailed_result
detailed_result[0]['entities']
detailed_result[0]['entities'][0].result
chunks=[]
entities=[]
for n in detailed_result[0]['entities']:
chunks.append... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Use pretrained match_chunk Pipeline for Individual Noun Phrase **Stages**- DocumentAssembler- SentenceDetector- Tokenizer- Part of Speech- ChunkerPipeline:- The pipeline uses regex `?*+`- which states that whenever the chunk finds an optional determiner (DT) followed by any number of adjectives (JJ) and then a noun (N... | pipeline = PretrainedPipeline('match_chunks', lang='en')
pipeline.model.stages
result = pipeline.annotate("The book has many chapters") # single noun phrase
result
result['chunk']
result = pipeline.annotate("the little yellow dog barked at the cat") #multiple noune phrases
result
result['chunk'] | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Extract exact dates from referential date phrases | pipeline = PretrainedPipeline('match_datetime', lang='en')
result = pipeline.annotate("I saw him yesterday and he told me that he will visit us next week")
result
detailed_result = pipeline.fullAnnotate("I saw him yesterday and he told me that he will visit us next week")
detailed_result
tuples = []
for x in detail... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Sentiment Analysis Vivek algopaper: `Fast and accurate sentiment classification using an enhanced Naive Bayes model`https://arxiv.org/abs/1305.6143code `https://github.com/vivekn/sentiment` | sentiment = PretrainedPipeline('analyze_sentiment', lang='en')
result = sentiment.annotate("The movie I watched today was not a good one")
result['sentiment'] | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
DL version (trained on imdb) | sentiment_imdb = PretrainedPipeline('analyze_sentimentdl_use_imdb', lang='en')
sentiment_imdb_glove = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang='en')
comment = '''
It's a very scary film but what impressed me was how true the film sticks to the original's tricks; it isn't filled with loud in-your-face j... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Public/1.SparkNLP_Basics.ipynb | HashamUlHaq/spark-nlp-workshop |
Table of Contents1 Preamble1.1 Some general parameters1.2 Functions1.2.0.1 Create overlap1.2.0.2 Activity representation2 The example Preamble | import pprint
import subprocess
import sys
sys.path.append('../')
import numpy as np
import scipy as sp
import statsmodels.api as sm
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
import seaborn as sns
%matplotlib in... | _____no_output_____ | MIT | jupyter/2018-09-20(Graph of non-orthogonal sequences example).ipynb | h-mayorquin/sequence_learning_paper |
Some general parameters | epsilon = 10e-80
vmin = -3.0
remove = 0.010
strict_maximum = True
dt = 0.001
tau_s = 0.010
tau_a = 0.250
g_I = 2.0
g_a = 2.0
G = 50.0
sns.set(font_scale=3.5)
sns.set_style("whitegrid", {'axes.grid': False})
plt.rcParams['figure.figsize'] = (12, 8)
lw = 10
ms = 22
alpha_graph = 0.3
colors = sns.color_palette() | _____no_output_____ | MIT | jupyter/2018-09-20(Graph of non-orthogonal sequences example).ipynb | h-mayorquin/sequence_learning_paper |
Functions Create overlap | from copy import deepcopy
def create_overalaped_representation(manager, representation_overlap, sequence_overlap):
x = deepcopy(manager.canonical_activity_representation)
to_modify = int(representation_overlap * len(x[0]))
sequence_size = int(0.5 * len(x))
sequence_overlap_size = int(sequence_overlap ... | _____no_output_____ | MIT | jupyter/2018-09-20(Graph of non-orthogonal sequences example).ipynb | h-mayorquin/sequence_learning_paper |
Activity representation | activity_representation = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5],
[10, 10, 10], [11, 11, 11], [2, 2, 12], [3, 3, 13], [14, 14, 14], [15, 15, 15]]) | _____no_output_____ | MIT | jupyter/2018-09-20(Graph of non-orthogonal sequences example).ipynb | h-mayorquin/sequence_learning_paper |
The example | sigma_out = 0.0
tau_z_pre = 0.025
tau_z_post = 0.020
hypercolumns = 3
minicolumns = 20
n_patterns = 12
patterns_per_sequence = 6
representation_overlap = 0.75
sequence_overlap = 0.5
# Training protocol
training_times_base = 0.100
training_times = [training_times_base for i in range(n_patterns)]
ipi_base = 0.0
inter_p... | _____no_output_____ | MIT | jupyter/2018-09-20(Graph of non-orthogonal sequences example).ipynb | h-mayorquin/sequence_learning_paper |
Abstract - Goal: Learn python basics on Jupyter notebook.- Ref: - Python crash course (Google for it) - the [naming section](https://github.com/google/styleguide/blob/gh-pages/pyguide.md316-naming) of Google's python style guide Python on Jupyter notebook - ColabPython on Jupyter notebook allows one to perfor... | 2 + 2 #Executing a Command | _____no_output_____ | MIT | other/python_notebook.ipynb | nickwotton/MQP2019 |
Importing LibrariesThe vast majority of the time, you'll want to use functions from pre-built libraries. You can't import every library each time, but you can import most of the common scientific ones. Here I import numpy, pandas, and matplotlib, the most common and useful libraries. Notice that you can rename librari... | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt | _____no_output_____ | MIT | other/python_notebook.ipynb | nickwotton/MQP2019 |
Using numpy and matplotlib: A sampling example Let's use numpy to select 100 standard normal radom variables, and make a plot. | X = np.random.normal(0, 1, 100)
plt.plot(X);
plt.xlabel('Time')
plt.ylabel('Returns')
plt.show() | _____no_output_____ | MIT | other/python_notebook.ipynb | nickwotton/MQP2019 |
Let's use `numpy` to take some simple statistics. | np.mean(X)
np.std(X)
| _____no_output_____ | MIT | other/python_notebook.ipynb | nickwotton/MQP2019 |
BiDirectional LSTM classifier in keras Load dependencies | import keras
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, SpatialDropout1D, Dense, Flatten, Dropout, LSTM
from keras.layers.wrappers import Bidirectional
from keras.callbacks import ModelCheckpoint # new!
... | _____no_output_____ | Apache-2.0 | Deep Learning/NLP Tutorials/.ipynb_checkpoints/Conv_LSTM_Stack_in_keras-checkpoint.ipynb | surajch77/ML-Examples |
Load data For a given data set: * the Keras text utilities [here](https://keras.io/preprocessing/text/) quickly preprocess natural language and convert it into an index* the `keras.preprocessing.text.Tokenizer` class may do everything you need in one line: * tokenize into words or characters * `num_words`: maxim... | (x_train, y_train), (x_valid, y_valid) = imdb.load_data(num_words=n_unique_words) | _____no_output_____ | Apache-2.0 | Deep Learning/NLP Tutorials/.ipynb_checkpoints/Conv_LSTM_Stack_in_keras-checkpoint.ipynb | surajch77/ML-Examples |
Preprocess data | x_train = pad_sequences(x_train, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)
x_valid = pad_sequences(x_valid, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)
x_train[:6]
for i in range(6):
print len(x_train[i]) | 200
200
200
200
200
200
| Apache-2.0 | Deep Learning/NLP Tutorials/.ipynb_checkpoints/Conv_LSTM_Stack_in_keras-checkpoint.ipynb | surajch77/ML-Examples |
Design neural network architecture | model = Sequential()
model.add(Embedding(n_unique_words, n_dim, input_length=max_review_length))
model.add(SpatialDropout1D(drop_embed))
# model.add(Conv1D(n_conv, k_conv, activation='relu'))
# model.add(Conv1D(n_conv, k_conv, activation='relu'))
# model.add(GlobalMaxPooling1D())
# model.add(Dense(n_dense, activation=... | _____no_output_____ | Apache-2.0 | Deep Learning/NLP Tutorials/.ipynb_checkpoints/Conv_LSTM_Stack_in_keras-checkpoint.ipynb | surajch77/ML-Examples |
Configure Model | model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
modelcheckpoint = ModelCheckpoint(filepath=output_dir+"/weights.{epoch:02d}.hdf5")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
verbose=1, vali... | Train on 25000 samples, validate on 25000 samples
Epoch 1/6
25000/25000 [==============================] - 689s 28ms/step - loss: 0.5523 - acc: 0.7024 - val_loss: 0.3817 - val_acc: 0.8368
Epoch 2/6
640/25000 [..............................] - ETA: 7:21 - loss: 0.2883 - acc: 0.8953 | Apache-2.0 | Deep Learning/NLP Tutorials/.ipynb_checkpoints/Conv_LSTM_Stack_in_keras-checkpoint.ipynb | surajch77/ML-Examples |
Evaluate | model.load_weights(output_dir+"/weights.01.hdf5") # zero-indexed
y_hat = model.predict_proba(x_valid)
len(y_hat)
y_hat[0]
plt.hist(y_hat)
_ = plt.axvline(x=0.5, color='orange')
pct_auc = roc_auc_score(y_valid, y_hat)*100.0
"{:0.2f}".format(pct_auc) | _____no_output_____ | Apache-2.0 | Deep Learning/NLP Tutorials/.ipynb_checkpoints/Conv_LSTM_Stack_in_keras-checkpoint.ipynb | surajch77/ML-Examples |
Header1 | df = pd.read_csv('../data/raw/yellow_tripdata_2015-12-sample.csv')
df.head()
df['delta_lon'] = df.pickup_longitude - df.dropoff_longitude
df['delta_lat'] = df.pickup_latitude - df.dropoff_latitude
def calculate_direction(d_lon, d_lat):
result = np.zeros(len(d_lon))
l = np.sqrt(d_lon**2 + d_lat**2)
result[d... | 4.7793882127762615
| Unlicense | notebooks/random_forest.ipynb | AshwathSalimath/amazon-lab |
Homework (15 pts) - Classification | # Questions are based on the mouse cortex protein expression level dataset used in lecture.
# The Data_Cortex_Nuclear.csv file is available in the same folder as this notebook
# or at https://www.kaggle.com/ruslankl/mice-protein-expression
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from skle... | _____no_output_____ | Unlicense | homework/homework_classification.ipynb | sathwikiyer/NEU-365P-385L-Spring-2021 |
---1. (3 pts) Remove mice/samples with missing measurements from the dataframe. Store the result in the mice variable for use with subsequent questions. | mice.isnull().sum()
mice = mice.dropna() | _____no_output_____ | Unlicense | homework/homework_classification.ipynb | sathwikiyer/NEU-365P-385L-Spring-2021 |
---2. (3 pts) Use both logistic regression and a random forest to classify the mice as either memantine or saline treated based on their protein expression profiles. Contrast the overall accuracy of the two classifiers on a withheld test set. You can ignore any warnings similar to those seen in the lecture for the purp... | X = mice.loc[:,'DYRK1A_N':'CaNA_N'] # just protein expression levels
y = mice['Treatment']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 1)
model = LogisticRegression()
model.fit(X_train, y_train)
LogisticRegression()
y_test_pred = model.predict(X_test)
lr_treatment_accu... | Random forests are a better predictor
| Unlicense | homework/homework_classification.ipynb | sathwikiyer/NEU-365P-385L-Spring-2021 |
---3. (3 pts) For the two classifiers trained in 2 above, contrast their confusion matrices based on predictions for a withheld test set. | cm = confusion_matrix(y_test, y_test_pred)
lr_treatment_cmd = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = model.classes_)
lr_treatment_cmd.plot();
cm = confusion_matrix(y_test, y_test_pred2)
rf_treatment_cmd = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = model.classes_)
rf_treatm... | _____no_output_____ | Unlicense | homework/homework_classification.ipynb | sathwikiyer/NEU-365P-385L-Spring-2021 |
---4. (3 pts) For the two classifiers trained in 2 above, contrast their ROC curves based on predictions for Memantine treatment in withheld test set. | y_test_proba = model.predict_proba(X_test)
fpr, tpr, thresholds = roc_curve(y_test, y_test_proba[:,1], pos_label = model.classes_[1])
plt.plot(fpr, tpr, color = 'r', label = model.classes_[0])
plt.plot([0, 1], [0, 1], color = 'k', linestyle = '--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt... | _____no_output_____ | Unlicense | homework/homework_classification.ipynb | sathwikiyer/NEU-365P-385L-Spring-2021 |
---5. (3 pts) Use a random forest to classify the mice into each of the 8 classes defined by genotype, fear conditioning and treatment based on their protein expression profiles. Repeat this classification 77 separate times, where on the ith time you will use only the first i proteins. For example, the 1st time you wil... | proteins = X_train.shape[1]
accuracies = np.zeros((proteins,))
rFor = RandomForestClassifier(n_estimators = 100, random_state = 1)
for i in np.arange(proteins) + 1:
pTrain = X_train.iloc[:, 0:i]
pTest = X_test.iloc[:, 0:i]
rFor.fit(pTrain, y_train)
rForPred = rFor.predict(pTest)
rForAcc = accuracy... | _____no_output_____ | Unlicense | homework/homework_classification.ipynb | sathwikiyer/NEU-365P-385L-Spring-2021 |
Assignment 2: Parts-of-Speech Tagging (POS)Welcome to the second assignment of Course 2 in the Natural Language Processing specialization. This assignment will develop skills in part-of-speech (POS) tagging, the process of assigning a part-of-speech tag (Noun, Verb, Adjective...) to each word in an input text. Taggin... | # Importing packages and loading in the data set
from utils_pos import get_word_tag, preprocess
import pandas as pd
from collections import defaultdict
import math
import numpy as np | _____no_output_____ | MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Part 0: Data SourcesThis assignment will use two tagged data sets collected from the **Wall Street Journal (WSJ)**. [Here](http://relearn.be/2015/training-common-sense/sources/software/pattern-2.6-critical-fork/docs/html/mbsp-tags.html) is an example 'tag-set' or Part of Speech designation describing the two or three ... | # load in the training corpus
with open("WSJ_02-21.pos", 'r') as f:
training_corpus = f.readlines()
print(f"A few items of the training corpus list")
print(training_corpus[0:5])
# read the vocabulary data, split by each line of text, and save the list
with open("hmm_vocab.txt", 'r') as f:
voc_l = f.read().spli... | The length of the preprocessed test corpus: 34199
This is a sample of the test_corpus:
['The', 'economy', "'s", 'temperature', 'will', 'be', 'taken', 'from', 'several', '--unk--']
| MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Part 1: Parts-of-speech tagging Part 1.1 - TrainingYou will start with the simplest possible parts-of-speech tagger and we will build up to the state of the art. In this section, you will find the words that are not ambiguous. - For example, the word `is` is a verb and it is not ambiguous. - In the `WSJ` corpus, $86$... | # UNQ_C1 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: create_dictionaries
def create_dictionaries(training_corpus, vocab):
"""
Input:
training_corpus: a corpus where each line has a word followed by its tag.
vocab: a dictionary where keys are words in vocabulary and value is an inde... | Number of POS tags (number of 'states'): 46
View these POS tags (states)
['#', '$', "''", '(', ')', ',', '--s--', '.', ':', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN',... | MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Expected Output```CPPNumber of POS tags (number of 'states'46View these states['', '$', "''", '(', ')', ',', '--s--', '.', ':', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', '... | print("transition examples: ")
for ex in list(transition_counts.items())[:3]:
print(ex)
print()
print("emission examples: ")
for ex in list(emission_counts.items())[200:203]:
print (ex)
print()
print("ambiguous word example: ")
for tup,cnt in emission_counts.items():
if tup[1] == 'back': print (tup, cnt) | transition examples:
(('--s--', 'IN'), 5050)
(('IN', 'DT'), 32364)
(('DT', 'NNP'), 9044)
emission examples:
(('DT', 'any'), 721)
(('NN', 'decrease'), 7)
(('NN', 'insider-trading'), 5)
ambiguous word example:
('RB', 'back') 304
('VB', 'back') 20
('RP', 'back') 84
('JJ', 'back') 25
('NN', 'back') 29
('VBP', 'back') ... | MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Expected Output```CPPtransition examples: (('--s--', 'IN'), 5050)(('IN', 'DT'), 32364)(('DT', 'NNP'), 9044)emission examples: (('DT', 'any'), 721)(('NN', 'decrease'), 7)(('NN', 'insider-trading'), 5)ambiguous word example: ('RB', 'back') 304('VB', 'back') 20('RP', 'back') 84('JJ', 'back') 25('NN', 'back') 29('VBP', 'b... | # UNQ_C2 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: predict_pos
def predict_pos(prep, y, emission_counts, vocab, states):
'''
Input:
prep: a preprocessed version of 'y'. A list with the 'word' component of the tuples.
y: a corpus composed of a list of tuples where each tuple cons... | Accuracy of prediction using predict_pos is 0.8889
| MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Expected Output```CPPAccuracy of prediction using predict_pos is 0.8889```88.9% is really good for this warm up exercise. With hidden markov models, you should be able to get **95% accuracy.** Part 2: Hidden Markov Models for POSNow you will build something more context specific. Concretely, you will be implementing ... | # UNQ_C3 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: create_transition_matrix
def create_transition_matrix(alpha, tag_counts, transition_counts):
'''
Input:
alpha: number used for smoothing
tag_counts: a dictionary mapping each tag to its respective count
transition_counts... | A at row 0, col 0: 0.000007040
A at row 3, col 1: 0.1691
View a subset of transition matrix A
RBS RP SYM TO UH
RBS 2.217069e-06 2.217069e-06 2.217069e-06 0.008870 2.217069e-06
RP 3.756509e-07 7.516775e-04 3.756509e-07 0.051089 3.756509e-07
SYM 1.722772e-0... | MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Expected Output```CPPA at row 0, col 0: 0.000007040A at row 3, col 1: 0.1691View a subset of transition matrix A RBS RP SYM TO UHRBS 2.217069e-06 2.217069e-06 2.217069e-06 0.008870 2.217069e-06RP 3.756509e-07 7.516775e-04 3.756509e-07 0.051089 3.756509e-07... | # UNQ_C4 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: create_emission_matrix
def create_emission_matrix(alpha, tag_counts, emission_counts, vocab):
'''
Input:
alpha: tuning parameter used in smoothing
tag_counts: a dictionary mapping each tag to its respective count
emissi... | View Matrix position at row 0, column 0: 0.000006032
View Matrix position at row 3, column 1: 0.000000720
725 adroitly engineers promoted synergy
CD 8.201296e-05 2.732854e-08 2.732854e-08 2.732854e-08 2.732854e-08
NN 7.521128e-09 7.521128e-09 7.521128e-09 7.521128e-09 2.257... | MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Expected Output```CPPView Matrix position at row 0, column 0: 0.000006032View Matrix position at row 3, column 1: 0.000000720 725 adroitly engineers promoted synergyCD 8.201296e-05 2.732854e-08 2.732854e-08 2.732854e-08 2.732854e-08NN 7.521128e-09 7.521128e-09 7.521128e-09 7... | # UNQ_C5 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: initialize
def initialize(states, tag_counts, A, B, corpus, vocab):
'''
Input:
states: a list of all possible parts-of-speech
tag_counts: a dictionary mapping each tag to its respective count
A: Transition Matrix of dimen... | best_probs[0,0]: -22.6098
best_paths[2,3]: 0.0000
| MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Expected Output```CPPbest_probs[0,0]: -22.6098best_paths[2,3]: 0.0000``` Part 3.2 Viterbi ForwardIn this part of the assignment, you will implement the `viterbi_forward` segment. In other words, you will populate your `best_probs` and `best_paths` matrices.- Walk forward through the corpus.- For each word, compute a ... | # UNQ_C6 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: viterbi_forward
def viterbi_forward(A, B, test_corpus, best_probs, best_paths, vocab):
'''
Input:
A, B: The transition and emission matrices respectively
test_corpus: a list containing a preprocessed corpus
best_probs: an... | _____no_output_____ | MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Run the `viterbi_forward` function to fill in the `best_probs` and `best_paths` matrices.**Note** that this will take a few minutes to run. There are about 30,000 words to process. | # this will take a few minutes to run => processes ~ 30,000 words
best_probs, best_paths = viterbi_forward(A, B, prep, best_probs, best_paths, vocab)
# Test this function
print(f"best_probs[0,1]: {best_probs[0,1]:.4f}")
print(f"best_probs[0,4]: {best_probs[0,4]:.4f}") | best_probs[0,1]: -24.7822
best_probs[0,4]: -49.5601
| MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Expected Output```CPPbest_probs[0,1]: -24.7822best_probs[0,4]: -49.5601``` Part 3.3 Viterbi backwardNow you will implement the Viterbi backward algorithm.- The Viterbi backward algorithm gets the predictions of the POS tags for each word in the corpus using the `best_paths` and the `best_probs` matrices.The example b... | # UNQ_C7 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: viterbi_backward
def viterbi_backward(best_probs, best_paths, corpus, states):
'''
This function returns the best path.
'''
# Get the number of words in the corpus
# which is also the number of columns in best_probs, best_paths
... | The prediction for pred[-7:m-1] is:
['see', 'them', 'here', 'with', 'us', '.']
['VB', 'PRP', 'RB', 'IN', 'PRP', '.']
The prediction for pred[0:8] is:
['DT', 'NN', 'POS', 'NN', 'MD', 'VB', 'VBN']
['The', 'economy', "'s", 'temperature', 'will', 'be', 'taken']
| MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
**Expected Output:** ```CPPThe prediction for pred[-7:m-1] is: ['see', 'them', 'here', 'with', 'us', '.'] ['VB', 'PRP', 'RB', 'IN', 'PRP', '.'] The prediction for pred[0:8] is: ['DT', 'NN', 'POS', 'NN', 'MD', 'VB', 'VBN'] ['The', 'economy', "'s", 'temperature', 'will', 'be', 'taken'] ```Now you just have... | print('The third word is:', prep[3])
print('Your prediction is:', pred[3])
print('Your corresponding label y is: ', y[3]) | The third word is: temperature
Your prediction is: NN
Your corresponding label y is: temperature NN
| MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Exercise 08Implement a function to compute the accuracy of the viterbi algorithm's POS tag predictions.- To split y into the word and its tag you can use `y.split()`. | # UNQ_C8 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: compute_accuracy
def compute_accuracy(pred, y):
'''
Input:
pred: a list of the predicted parts-of-speech
y: a list of lines where each word is separated by a '\t' (i.e. word \t tag)
Output:
'''
num_correct ... | Accuracy of the Viterbi algorithm is 0.9531
| MIT | Course 2 - Natural Language Processing with Probabilistic Models/Week 2/C2_W2_Assignment.ipynb | naveen-9697/Natural-Language-Specialisation---Coursera |
Dependencies for this notebook:* pip install spacy, pandas, matplotlib* python -m spacy.en.download | from IPython.display import SVG, display
import spacy
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
%matplotlib inline
#encode some text as uncode
text = u"I'm executing this code on an Apple Computer."
#instantiate a language model
#to download language model: python -m spacy.en.do... | _____no_output_____ | MIT | spacy-examples/Introduction to Natural Language Processing with SpaCy - Working Files/Chapter 2/Working-with-SpaCy-for-the-first-time_Part_1.ipynb | peterwilliams97/spaCy_practice |
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