markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
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Data contains: link to wikipedia article, name of person, text of article. | people.head()
len(people) | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Explore the dataset and checkout the text it contains
Exploring the entry for president Obama | obama = people[people['name'] == 'Barack Obama']
obama
obama['text'] | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Exploring the entry for actor George Clooney | clooney = people[people['name'] == 'George Clooney']
clooney['text'] | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Get the word counts for Obama article | obama['word_count'] = graphlab.text_analytics.count_words(obama['text'])
print obama['word_count'] | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Sort the word counts for the Obama article
Turning dictonary of word counts into a table | obama_word_count_table = obama[['word_count']].stack('word_count', new_column_name = ['word','count']) | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Sorting the word counts to show most common words at the top | obama_word_count_table.head()
obama_word_count_table.sort('count',ascending=False) | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Most common words include uninformative words like "the", "in", "and",...
Compute TF-IDF for the corpus
To give more weight to informative words, we weigh them by their TF-IDF scores. | people['word_count'] = graphlab.text_analytics.count_words(people['text'])
people.head()
tfidf = graphlab.text_analytics.tf_idf(people['word_count'])
# Earlier versions of GraphLab Create returned an SFrame rather than a single SArray
# This notebook was created using Graphlab Create version 1.7.1
if graphlab.version... | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Examine the TF-IDF for the Obama article | obama = people[people['name'] == 'Barack Obama']
obama[['tfidf']].stack('tfidf',new_column_name=['word','tfidf']).sort('tfidf',ascending=False) | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Words with highest TF-IDF are much more informative.
Manually compute distances between a few people
Let's manually compare the distances between the articles for a few famous people. | clinton = people[people['name'] == 'Bill Clinton']
beckham = people[people['name'] == 'David Beckham'] | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Is Obama closer to Clinton than to Beckham?
We will use cosine distance, which is given by
(1-cosine_similarity)
and find that the article about president Obama is closer to the one about former president Clinton than that of footballer David Beckham. | graphlab.distances.cosine(obama['tfidf'][0],clinton['tfidf'][0])
graphlab.distances.cosine(obama['tfidf'][0],beckham['tfidf'][0]) | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Build a nearest neighbor model for document retrieval
We now create a nearest-neighbors model and apply it to document retrieval. | knn_model = graphlab.nearest_neighbors.create(people,features=['tfidf'],label='name')
knn_model.summary() | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Applying the nearest-neighbors model for retrieval
Who is closest to Obama? | knn_model.query(obama) | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
As we can see, president Obama's article is closest to the one about his vice-president Biden, and those of other politicians.
Other examples of document retrieval | swift = people[people['name'] == 'Taylor Swift']
knn_model.query(swift)
jolie = people[people['name'] == 'Angelina Jolie']
knn_model.query(jolie)
arnold = people[people['name'] == 'Arnold Schwarzenegger']
knn_model.query(arnold)
elton = people[people['name'] == 'Elton John']
elton
elton[['word_count']].stack('w... | machine-learning/foundations/document-retrieval-assignment.ipynb | scoaste/showcase | mit |
Sparsity preserving clustering Keras example
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/model_optimization/guide/combine/sparse_clustering_example"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<... | ! pip install -q tensorflow-model-optimization
import tensorflow as tf
import numpy as np
import tempfile
import zipfile
import os | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Train a tf.keras model for MNIST to be pruned and clustered | # Load MNIST dataset
mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images = test_images / 255.0
model = tf.keras.Sequential([
tf.keras.laye... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Evaluate the baseline model and save it for later usage | _, baseline_model_accuracy = model.evaluate(
test_images, test_labels, verbose=0)
print('Baseline test accuracy:', baseline_model_accuracy)
_, keras_file = tempfile.mkstemp('.h5')
print('Saving model to: ', keras_file)
tf.keras.models.save_model(model, keras_file, include_optimizer=False) | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Prune and fine-tune the model to 50% sparsity
Apply the prune_low_magnitude() API to prune the whole pre-trained model to achieve the model that is to be clustered in the next step. For how best to use the API to achieve the best compression rate while maintaining your target accuracy, refer to the pruning comprehensiv... | import tensorflow_model_optimization as tfmot
prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
pruning_params = {
'pruning_schedule': tfmot.sparsity.keras.ConstantSparsity(0.5, begin_step=0, frequency=100)
}
callbacks = [
tfmot.sparsity.keras.UpdatePruningStep()
]
pruned_model = prune_low_ma... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Fine-tune the model, check sparsity, and evaluate the accuracy against baseline
Fine-tune the model with pruning for 3 epochs. | # Fine-tune model
pruned_model.fit(
train_images,
train_labels,
epochs=3,
validation_split=0.1,
callbacks=callbacks) | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Define helper functions to calculate and print the sparsity of the model. | def print_model_weights_sparsity(model):
for layer in model.layers:
if isinstance(layer, tf.keras.layers.Wrapper):
weights = layer.trainable_weights
else:
weights = layer.weights
for weight in weights:
if "kernel" not in weight.name or "centroid" in weigh... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Check that the model kernels was correctly pruned. We need to strip the pruning wrapper first. We also create a deep copy of the model to be used in the next step. | stripped_pruned_model = tfmot.sparsity.keras.strip_pruning(pruned_model)
print_model_weights_sparsity(stripped_pruned_model)
stripped_pruned_model_copy = tf.keras.models.clone_model(stripped_pruned_model)
stripped_pruned_model_copy.set_weights(stripped_pruned_model.get_weights()) | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Apply clustering and sparsity preserving clustering and check its effect on model sparsity in both cases
Next, we apply both clustering and sparsity preserving clustering on the pruned model and observe that the latter preserves sparsity on your pruned model. Note that we stripped pruning wrappers from the pruned model... | # Clustering
cluster_weights = tfmot.clustering.keras.cluster_weights
CentroidInitialization = tfmot.clustering.keras.CentroidInitialization
clustering_params = {
'number_of_clusters': 8,
'cluster_centroids_init': CentroidInitialization.KMEANS_PLUS_PLUS
}
clustered_model = cluster_weights(stripped_pruned_model, *... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Check sparsity for both models. | print("Clustered Model sparsity:\n")
print_model_weights_sparsity(clustered_model)
print("\nSparsity preserved clustered Model sparsity:\n")
print_model_weights_sparsity(sparsity_clustered_model) | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Create 1.6x smaller models from clustering
Define helper function to get zipped model file. | def get_gzipped_model_size(file):
# It returns the size of the gzipped model in kilobytes.
_, zipped_file = tempfile.mkstemp('.zip')
with zipfile.ZipFile(zipped_file, 'w', compression=zipfile.ZIP_DEFLATED) as f:
f.write(file)
return os.path.getsize(zipped_file)/1000
# Clustered model
clustered_model_file... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Create a TFLite model from combining sparsity preserving weight clustering and post-training quantization
Strip clustering wrappers and convert to TFLite. | stripped_sparsity_clustered_model = tfmot.clustering.keras.strip_clustering(sparsity_clustered_model)
converter = tf.lite.TFLiteConverter.from_keras_model(stripped_sparsity_clustered_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
sparsity_clustered_quant_model = converter.convert()
_, pruned_and_clustere... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
See the persistence of accuracy from TF to TFLite | def eval_model(interpreter):
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
# Run predictions on every image in the "test" dataset.
prediction_digits = []
for i, test_image in enumerate(test_images):
if i % 1000 == 0:
print(f"Ev... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
You evaluate the model, which has been pruned, clustered and quantized, and then see that the accuracy from TensorFlow persists in the TFLite backend. | # Keras model evaluation
stripped_sparsity_clustered_model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
_, sparsity_clustered_keras_accuracy = stripped_sparsity_clustered_model.evaluate(
test_images, test_labels, ve... | tensorflow_model_optimization/g3doc/guide/combine/sparse_clustering_example.ipynb | tensorflow/model-optimization | apache-2.0 |
Transform EEG data using current source density (CSD)
This script shows an example of how to use CSD
:footcite:PerrinEtAl1987,PerrinEtAl1989,Cohen2014,KayserTenke2015.
CSD takes the spatial Laplacian of the sensor signal (derivative in both
x and y). It does what a planar gradiometer does in MEG. Computing these
spatia... | # Authors: Alex Rockhill <aprockhill@mailbox.org>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path() | 0.24/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Load sample subject data | raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif')
raw = raw.pick_types(meg=False, eeg=True, eog=True, ecg=True, stim=True,
exclude=raw.info['bads']).load_data()
events = mne.find_events(raw)
raw.set_eeg_reference(projection=True).apply_proj() | 0.24/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Plot the raw data and CSD-transformed raw data: | raw_csd = mne.preprocessing.compute_current_source_density(raw)
raw.plot()
raw_csd.plot() | 0.24/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Also look at the power spectral densities: | raw.plot_psd()
raw_csd.plot_psd() | 0.24/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
CSD can also be computed on Evoked (averaged) data.
Here we epoch and average the data so we can demonstrate that. | event_id = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
'visual/right': 4, 'smiley': 5, 'button': 32}
epochs = mne.Epochs(raw, events, event_id=event_id, tmin=-0.2, tmax=.5,
preload=True)
evoked = epochs['auditory'].average() | 0.24/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
First let's look at how CSD affects scalp topography: | times = np.array([-0.1, 0., 0.05, 0.1, 0.15])
evoked_csd = mne.preprocessing.compute_current_source_density(evoked)
evoked.plot_joint(title='Average Reference', show=False)
evoked_csd.plot_joint(title='Current Source Density') | 0.24/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
CSD has parameters stiffness and lambda2 affecting smoothing and
spline flexibility, respectively. Let's see how they affect the solution: | fig, ax = plt.subplots(4, 4)
fig.subplots_adjust(hspace=0.5)
fig.set_size_inches(10, 10)
for i, lambda2 in enumerate([0, 1e-7, 1e-5, 1e-3]):
for j, m in enumerate([5, 4, 3, 2]):
this_evoked_csd = mne.preprocessing.compute_current_source_density(
evoked, stiffness=m, lambda2=lambda2)
this... | 0.24/_downloads/1537c1215a3e40187a4513e0b5f1d03d/eeg_csd.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Subsetting data
Subset variables (columns)
For a DataFrame, basic indexing selects the columns (cfr. the dictionaries of pure python)
Selecting a single column: | countries['area'] # single [] | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
Remember that the same syntax can also be used to add a new columns: df['new'] = ....
We can also select multiple columns by passing a list of column names into []: | countries[['area', 'population']] # double [[]] | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
Subset observations (rows)
Using [], slicing or boolean indexing accesses the rows:
Slicing | countries[0:4] | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
Boolean indexing (filtering)
Often, you want to select rows based on a certain condition. This can be done with 'boolean indexing' (like a where clause in SQL) and comparable to numpy.
The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed. | countries['area'] > 100000
countries[countries['area'] > 100000]
countries[countries['population'] > 50] | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
An overview of the possible comparison operations:
Operator | Description
------ | --------
== | Equal
!= | Not equal
> | Greater than
>= | Greater than or equal
\< | Lesser than
<= | Lesser than or equal
and to combine multiple conditions:
Operator | Description
------ | -----... | s = countries['capital']
s.isin?
s.isin(['Berlin', 'London']) | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
This can then be used to filter the dataframe with boolean indexing: | countries[countries['capital'].isin(['Berlin', 'London'])] | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
Let's say we want to select all data for which the capital starts with a 'B'. In Python, when having a string, we could use the startswith method: | string = 'Berlin'
string.startswith('B') | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
In pandas, these are available on a Series through the str namespace: | countries['capital'].str.startswith('B') | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
For an overview of all string methods, see: https://pandas.pydata.org/pandas-docs/stable/reference/series.html#string-handling
Exercises using the Titanic dataset | df = pd.read_csv("data/titanic.csv")
df.head() | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 1</b>:
<ul>
<li>Select all rows for male passengers and calculate the mean age of those passengers. Do the same for the female passengers.</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data1.py
# %load _solutions/pandas_03a_selecting_data2.py
# %load _solutions/pandas_03a_selecting_data3.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
We will later see an easier way to calculate both averages at the same time with groupby.
<div class="alert alert-success">
<b>EXERCISE 2</b>:
<ul>
<li>How many passengers older than 70 were on the Titanic?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data4.py
# %load _solutions/pandas_03a_selecting_data5.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 3</b>:
<ul>
<li>Select the passengers that are between 30 and 40 years old?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data6.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 4</b>:
For a single string `name = 'Braund, Mr. Owen Harris'`, split this string (check the `split()` method of a string) and get the first element of the resulting list.
<details><summary>Hints</summary>
- No Pandas in this exercise, just standard Python.
- The `split(... | name = 'Braund, Mr. Owen Harris'
# %load _solutions/pandas_03a_selecting_data7.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 5</b>:
Convert the solution of the previous exercise to all strings of the `Name` column at once. Split the 'Name' column on the `,`, extract the first part (the surname), and add this as new column 'Surname'.
<details><summary>Hints</summary>
- Pandas uses the `str` a... | # %load _solutions/pandas_03a_selecting_data8.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 6</b>:
<ul>
<li>Select all passenger that have a surname starting with 'Williams'.</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data9.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 7</b>:
<ul>
<li>Select all rows for the passengers with a surname of more than 15 characters.</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data10.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
[OPTIONAL] more exercises
For the quick ones among you, here are some more exercises with some larger dataframe with film data. These exercises are based on the PyCon tutorial of Brandon Rhodes (so all credit to him!) and the datasets he prepared for that. You can download these data from here: titles.csv and cast.csv ... | cast = pd.read_csv('data/cast.csv')
cast.head()
titles = pd.read_csv('data/titles.csv')
titles.head() | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 8</b>:
<ul>
<li>How many movies are listed in the titles dataframe?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data11.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 9</b>:
<ul>
<li>What are the earliest two films listed in the titles dataframe?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data12.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 10</b>:
<ul>
<li>How many movies have the title "Hamlet"?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data13.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 11</b>:
<ul>
<li>List all of the "Treasure Island" movies from earliest to most recent.</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data14.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 12</b>:
<ul>
<li>How many movies were made from 1950 through 1959?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data15.py
# %load _solutions/pandas_03a_selecting_data16.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 13</b>:
<ul>
<li>How many roles in the movie "Inception" are NOT ranked by an "n" value?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data17.py
# %load _solutions/pandas_03a_selecting_data18.py
# %load _solutions/pandas_03a_selecting_data19.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 14</b>:
<ul>
<li>But how many roles in the movie "Inception" did receive an "n" value?</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data20.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 15</b>:
<ul>
<li>Display the cast of the "Titanic" (the most famous 1997 one) in their correct "n"-value order, ignoring roles that did not earn a numeric "n" value.</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data21.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
<div class="alert alert-success">
<b>EXERCISE 16</b>:
<ul>
<li>List the supporting roles (having n=2) played by Brad Pitt in the 1990s, in order by year.</li>
</ul>
</div> | # %load _solutions/pandas_03a_selecting_data22.py | notebooks/pandas_03a_selecting_data.ipynb | jorisvandenbossche/DS-python-data-analysis | bsd-3-clause |
(1) EX. Using the list of words you produced by splitting 'new_string', create a new list that contains only the words whose last letter is "y"
We can combine list comprehension and the string manipulation function .endswith(), both of which we learned about on Wednesday, to create a new list the keeps only the element... | word_list_y = [word for word in new_string_list if word.endswith('y')]
#print the new list
word_list_y | 01-IntroToPython/00-PythonBasics_ExerciseSolutions.ipynb | lknelson/text-analysis-2017 | bsd-3-clause |
(2) EX. Create a new list that contains the first letter of each word.
We can again use list comprehension, combined with string splicing, to produce a new list that contain only the first letter of each word. Remember in Python counting starts at 0. | word_list_firstletter = [word[0] for word in new_string_list]
#print our new list
word_list_firstletter | 01-IntroToPython/00-PythonBasics_ExerciseSolutions.ipynb | lknelson/text-analysis-2017 | bsd-3-clause |
(3) EX. Create a new list that contains only words longer than two letters.
We can, again, use list comprehension, the 'len' function, and the algorithm function greater than, or '>', to filter and keep words longer than two letters. Note that '>' is strictly greater than. If we wanted to include words with 2 letters ... | word_list_long = [n for n in new_string_list if len(n)>2]
#print new list
word_list_long | 01-IntroToPython/00-PythonBasics_ExerciseSolutions.ipynb | lknelson/text-analysis-2017 | bsd-3-clause |
Accessing Data
You can access DataFrame data using familiar Python dict/list operations: | cities = pd.DataFrame({'City Name': city_names, 'Population': population})
print(type(cities['City Name']))
cities['City Name']
print(type(cities["City Name"][1]))
cities["City Name"][1]
print(type(cities[0:2]))
cities[0:2] | machine learning/google-ml-crash-course/pandas/pandas_intro.ipynb | yavuzovski/playground | gpl-3.0 |
Manipulating Data
You may apply Python's basic arithmetic operations to Series. For example: | population / 1000
import numpy as np
np.log(population)
cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])
cities['Population density'] = cities['Population'] / cities['Area square miles']
cities
population.apply(lambda val: val > 1000000) | machine learning/google-ml-crash-course/pandas/pandas_intro.ipynb | yavuzovski/playground | gpl-3.0 |
Exercise #1
Modify the cities table by adding a new boolean column that is True if and only if both of the following are True:
The city is named after a saint.
The city has an area greater than 50 square miles.
Note: Boolean Series are combined using the bitwise, rather than the traditional boolean, operators. For ex... | cities['is saint and wide'] = (cities['Area square miles'] > 50) & (cities['City Name'].apply(lambda name: name.startswith("San")))
cities | machine learning/google-ml-crash-course/pandas/pandas_intro.ipynb | yavuzovski/playground | gpl-3.0 |
Indexes
Both Series and DataFrame objects also define an index property that assigns an identifier value to each Series item or DataFrame row.
By default, at construction, pandas assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change wh... | city_names.index
cities.index
cities.reindex([2, 0, 1]) | machine learning/google-ml-crash-course/pandas/pandas_intro.ipynb | yavuzovski/playground | gpl-3.0 |
Reindexing is a great way to shuffle (randomize) a DataFrame. In the example below, we take the index, which is array-like, and pass it to NumPy's random.permutation function, which shuffles its values in place. Calling reindex with this shuffled array causes the DataFrame rows to be shuffled in the same way. | cities.reindex(np.random.permutation(cities.index)) | machine learning/google-ml-crash-course/pandas/pandas_intro.ipynb | yavuzovski/playground | gpl-3.0 |
Exercise #2
The reindex method allows index values that are not in the original DataFrame's index values. Try it and see what happens if you use such values! Why do you think this is allowed? | cities.reindex([4, 2, 1, 3, 0]) | machine learning/google-ml-crash-course/pandas/pandas_intro.ipynb | yavuzovski/playground | gpl-3.0 |
1. Создание векторов
Самый простой способ создать вектор в NumPy — задать его явно с помощью numpy.array(list, dtype=None, ...).
Параметр list задает итерируемый объект, из которого можно создать вектор. Например, в качестве этого параметра можно задать список чисел. Параметр dtype задает тип значений вектора, например... | a = np.array([1, 2, 3, 4])
print 'Вектор:\n', a
b = np.array([1, 2, 3, 4, 5], dtype=float)
print 'Вещественный вектор:\n', b
c = np.array([True, False, True], dtype=bool)
print 'Булевский вектор:\n', c | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Тип значений вектора можно узнать с помощью numpy.ndarray.dtype: | print 'Тип булевского вектора:\n', c.dtype | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Другим способом задания вектора является функция numpy.arange(([start, ]stop, [step, ]...), которая задает последовательность чисел заданного типа из промежутка [start, stop) через шаг step: | d = np.arange(start=10, stop=20, step=2) # последнее значение не включается!
print 'Вектор чисел от 10 до 20 с шагом 2:\n', d
f = np.arange(start=0, stop=1, step=0.3, dtype=float)
print 'Вещественный вектор чисел от 0 до 1 с шагом 0.3:\n', f | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
По сути вектор в NumPy является одномерным массивом, что соответствует интуитивному определению вектора: | print c.ndim # количество размерностей
print c.shape # shape фактически задает длину вектора | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Обратите внимание: вектор _и одномерный массив тождественные понятия в NumPy. Помимо этого, также существуют понятия _вектор-столбец и вектор-строка, которые, несмотря на то что математически задают один и тот же объект, являются двумерными массивами и имеют другое значение поля shape (в этом случае поле состоит из дву... | a = np.array([1, 2, 3])
b = np.array([6, 5, 4])
k = 2
print 'Вектор a:', a
print 'Вектор b:', b
print 'Число k:', k
print 'Сумма a и b:\n', a + b
print 'Разность a и b:\n', a - b
print 'Покоординатное умножение a и b:\n', a * b
print 'Умножение вектора на число (осуществляется покоординатно):\n', k * a | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
3. Нормы векторов
Вспомним некоторые нормы, которые можно ввести в пространстве $\mathbb{R}^{n}$, и рассмотрим, с помощью каких библиотек и функций их можно вычислять в NumPy.
p-норма
p-норма (норма Гёльдера) для вектора $x = (x_{1}, \dots, x_{n}) \in \mathbb{R}^{n}$ вычисляется по формуле:
$$
\left\Vert x \right\Vert_... | from numpy.linalg import norm | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
$\ell_{1}$ норма
$\ell_{1}$ норма
(также известная как манхэттенское расстояние)
для вектора $x = (x_{1}, \dots, x_{n}) \in \mathbb{R}^{n}$ вычисляется по формуле:
$$
\left\Vert x \right\Vert_{1} = \sum_{i=1}^n \left| x_{i} \right|.
$$
Ей в функции numpy.linalg.norm(x, ord=None, ...) соответствует параметр ord=1. | a = np.array([1, 2, -3])
print 'Вектор a:', a
print 'L1 норма вектора a:\n', norm(a, ord=1) | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
$\ell_{2}$ норма
$\ell_{2}$ норма (также известная как евклидова норма)
для вектора $x = (x_{1}, \dots, x_{n}) \in \mathbb{R}^{n}$ вычисляется по формуле:
$$
\left\Vert x \right\Vert_{2} = \sqrt{\sum_{i=1}^n \left( x_{i} \right)^2}.
$$
Ей в функции numpy.linalg.norm(x, ord=None, ...) соответствует параметр ord=2. | a = np.array([1, 2, -3])
print 'Вектор a:', a
print 'L2 норма вектора a:\n', norm(a, ord=2) | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Более подробно о том, какие еще нормы (в том числе матричные) можно вычислить, см. документацию.
4. Расстояния между векторами
Для двух векторов $x = (x_{1}, \dots, x_{n}) \in \mathbb{R}^{n}$ и $y = (y_{1}, \dots, y_{n}) \in \mathbb{R}^{n}$ $\ell_{1}$ и $\ell_{2}$ раccтояния вычисляются по следующим формулам соответст... | a = np.array([1, 2, -3])
b = np.array([-4, 3, 8])
print 'Вектор a:', a
print 'Вектор b:', b
print 'L1 расстояние между векторами a и b:\n', norm(a - b, ord=1)
print 'L2 расстояние между векторами a и b:\n', norm(a - b, ord=2) | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Также расстояние между векторами можно посчитать с помощью функции scipy.spatial.distance.cdist(XA, XB, metric='euclidean', p=2, ...) из модуля SciPy, предназначенного для выполнения научных и инженерных расчётов. | from scipy.spatial.distance import cdist | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
scipy.spatial.distance.cdist(...) требует, чтобы размерность XA и XB была как минимум двумерная. По этой причине для использования этой функции необходимо преобразовать векторы, которые мы рассматриваем в этом ноутбуке, к вектор-строкам с помощью способов, которые мы рассмотрим ниже.
Параметры XA, XB — исходные вектор... | a = np.array([6, 3, -5])
b = np.array([-1, 0, 7])
print 'Вектор a:', a
print 'Его размерность:', a.shape
print 'Вектор b:', b
print 'Его размерность:', b.shape
a = a.reshape((1, 3))
b = b.reshape((1, 3))
print 'После применения метода reshape:\n'
print 'Вектор-строка a:', a
print 'Его размерность:', a.shape
print 'Век... | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Заметим, что после применения этого метода размерность полученных вектор-строк будет равна shape. Следующий метод позволяет сделать такое же преобразование, но не изменяет размерность исходного вектора.
В NumPy к размерностям объектов можно добавлять фиктивные оси с помощью np.newaxis. Для того, чтобы понять, как это... | d = np.array([3, 0, 8, 9, -10])
print 'Вектор d:', d
print 'Его размерность:', d.shape
print 'Вектор d с newaxis --> вектор-строка:\n', d[np.newaxis, :]
print 'Полученная размерность:', d[np.newaxis, :].shape
print 'Вектор d с newaxis --> вектор-столбец:\n', d[:, np.newaxis]
print 'Полученная размерность:', d[:, np.n... | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Важно, что np.newaxis добавляет к размерности ось, длина которой равна 1 (это и логично, так как количество элементов должно сохраняться). Таким образом, надо вставлять новую ось там, где нужна единица в размерности.
Теперь посчитаем расстояния с помощью scipy.spatial.distance.cdist(...), используя np.newaxis для прео... | a = np.array([6, 3, -5])
b = np.array([-1, 0, 7])
print 'Евклидово расстояние между a и b (через cdist):', cdist(a[np.newaxis, :],
b[np.newaxis, :],
metric='euclidean') | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Эта функция также позволяет вычислять попарные расстояния между множествами векторов. Например, пусть у нас имеется матрица размера $m_{A} \times n$. Мы можем рассматривать ее как описание некоторых $m_{A}$ наблюдений в $n$-мерном пространстве. Пусть также имеется еще одна аналогичная матрица размера $m_{B} \times n$, ... | a = np.array([0, 5, -1])
b = np.array([-4, 9, 3])
print 'Вектор a:', a
print 'Вектор b:', b | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Скалярное произведение в пространстве $\mathbb{R}^{n}$ для двух векторов $x = (x_{1}, \dots, x_{n})$ и $y = (y_{1}, \dots, y_{n})$ определяется как:
$$
\langle x, y \rangle = \sum_{i=1}^n x_{i} y_{i}.
$$
Скалярное произведение двух векторов можно вычислять с помощью функции numpy.dot(a, b, ...) или метода vec1.dot(vec2... | print 'Скалярное произведение a и b (через функцию):', np.dot(a, b)
print 'Скалярное произведение a и b (через метод):', a.dot(b) | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Длиной вектора $x = (x_{1}, \dots, x_{n}) \in \mathbb{R}^{n}$ называется квадратный корень из скалярного произведения, то есть длина равна евклидовой норме вектора:
$$
\left| x \right| = \sqrt{\langle x, x \rangle} = \sqrt{\sum_{i=1}^n x_{i}^2} = \left\Vert x \right\Vert_{2}.
$$
Теперь, когда мы знаем расстояние между... | cos_angle = np.dot(a, b) / norm(a) / norm(b)
print 'Косинус угла между a и b:', cos_angle
print 'Сам угол:', np.arccos(cos_angle) | 1 Mathematics and Python/Lectures notebooks/9 vector operations/vector_operations.ipynb | maxis42/ML-DA-Coursera-Yandex-MIPT | mit |
Initialize
Path of files
Parameters for ilastik
Parameters for cell detection
Parameters for blood vessel segmentation | # Set folder where data is stored
current_dir = os.path.abspath(os.getcwd())
folder = next(os.walk('.'))[1][0]
#print(os.path.exists(folder)) # testing
# ilastik parameters
classifier_file = folder + '/xbrain_vessel_seg_v7.ilp'
#print(classifier_file) # testing
#print(os.path.exists(classifier_file)) #... | Demo/xbrain-demo-dvid.ipynb | nerdslab/xbrain | apache-2.0 |
Load in the data | # load data
dvid = DVIDRemote({ "protocol": "http",
"host": "172.19.248.41:8000/",
})
chan = dvid.get_channel('a3afee0bf807466c9b7c3b0bbfd1acbd','grayscale')
print(chan)
input_data = dvid.get_cutout(chan,0,[0,2],[0,64],[390,454])#DVID here -- np.load(image_file)
# plot the 50th s... | Demo/xbrain-demo-dvid.ipynb | nerdslab/xbrain | apache-2.0 |
Ingest data and classifer into ilastik | # Compute time required for processing
start = time.time()
# Process the data to probability maps
probability_maps = xbrain.classify_pixel(input_data, classifier_file, threads=no_of_threads, ram=ram_size)
end = time.time()
print("\nElapsed time: %f minutes" % ((end - start)/60)) | Demo/xbrain-demo-dvid.ipynb | nerdslab/xbrain | apache-2.0 |
Display the results of ilastik | # pull down the coorisponding matricies
cell_prob_map = probability_maps[:, :, :, 2]
vessel_prob_map = probability_maps[:, :, :, 1]
print("cell_prob_map shape", cell_prob_map.shape)
ndp.plot(cell_prob_map, slice=50, cmap1='jet', alpha=0.5)
print("vessel_prob_map shape", vessel_prob_map.shape)
ndp.plot(vessel_prob_ma... | Demo/xbrain-demo-dvid.ipynb | nerdslab/xbrain | apache-2.0 |
Running different package for testing new algorithm | # reload packages for testing new algorithms
# import importlib
# importlib.reload(xbrain)
# Compute time required for processing
start = time.time()
# cell detection
centroids, cell_map = xbrain.detect_cells(cell_prob_map, cell_probability_threshold, stopping_criterion, initial_template_size, dilation_size, max_no_c... | Demo/xbrain-demo-dvid.ipynb | nerdslab/xbrain | apache-2.0 |
Display results of new algorithm | # show results
print("Vessel Segmentation")
ndp.plot(input_data, vessel_map, slice = 50, alpha = 0.5)
print("Cell Segmentation")
ndp.plot(input_data, cell_map, slice = 50, alpha = 0.5)
| Demo/xbrain-demo-dvid.ipynb | nerdslab/xbrain | apache-2.0 |
The directory where we store the data files: | basedir = "~/DataOceano/MyOcean/INSITU_GLO_NRT_OBSERVATIONS_013_030/monthly/" + str(year) + str(month).zfill(2) + '/'
basedir = os.path.expanduser(basedir) | PythonNotebooks/PlatformPlots/Read_drifter_data_2.ipynb | CopernicusMarineInsitu/INSTACTraining | mit |
Simple plot
Configuration
We start by defining some options for the scatter plot:
* the range of temperature that will be shown;
* the colormap;
* the ticks to be put on the colorbar. | tempmin, tempmax = 5., 30.
cmaptemp = plt.cm.RdYlBu_r
normtemp = colors.Normalize(vmin=tempmin, vmax=tempmax)
tempticks = np.arange(tempmin, tempmax+0.1,2.5) | PythonNotebooks/PlatformPlots/Read_drifter_data_2.ipynb | CopernicusMarineInsitu/INSTACTraining | mit |
Loop on the files
We create a loop on the netCDF files located in our directory. Longitude, latitude and dept are read from every file, while the temperature is not always available. For the plot, we only take the data with have a depth dimension equal to 1. | fig = plt.figure(figsize=(12, 8))
nfiles_notemp = 0
filelist = sorted(glob.glob(basedir+'*.nc'))
for datafiles in filelist:
with netCDF4.Dataset(datafiles) as nc:
lon = nc.variables['LONGITUDE'][:]
lat = nc.variables['LATITUDE'][:]
depth = nc.variables['DEPH'][:]
try:
... | PythonNotebooks/PlatformPlots/Read_drifter_data_2.ipynb | CopernicusMarineInsitu/INSTACTraining | mit |
We also counted how many files don't have the temperature variable: | print 'Number of files: ' + str(len(filelist))
print 'Number of files without temperature: ' + str(nfiles_notemp) | PythonNotebooks/PlatformPlots/Read_drifter_data_2.ipynb | CopernicusMarineInsitu/INSTACTraining | mit |
Plot on a map
Configuration of the projection
We choose a Robin projection centered on 0ºE, with a cruse ('c') resolution for the coastline. | m = Basemap(projection='moll', lon_0=0, resolution='c') | PythonNotebooks/PlatformPlots/Read_drifter_data_2.ipynb | CopernicusMarineInsitu/INSTACTraining | mit |
The rest of the configuration of the plot can be kept as it was.
Loop on the files
We can copy the part of the code used before. We need to add a line for the projection of the coordinates: lon, lat = m(lon, lat). After the loop we can add the coastline and the continents. | fig = plt.figure(figsize=(12, 8))
nfiles_notemp = 0
filelist = sorted(glob.glob(basedir+'*.nc'))
for datafiles in filelist:
with netCDF4.Dataset(datafiles) as nc:
lon = nc.variables['LONGITUDE'][:]
lat = nc.variables['LATITUDE'][:]
depth = nc.variables['DEPH'][:]
lon, lat ... | PythonNotebooks/PlatformPlots/Read_drifter_data_2.ipynb | CopernicusMarineInsitu/INSTACTraining | mit |
Basic workflow <a class="anchor" id="Basic-workflow"></a>
The pyro.contrib.epidemiology module provides a modeling language for a class of stochastic discrete-time discrete-count compartmental models, together with a number of black box inference algorithms to perform joint inference on global parameters and latent v... | class SimpleSIRModel(CompartmentalModel):
def __init__(self, population, recovery_time, data):
compartments = ("S", "I") # R is implicit.
duration = len(data)
super().__init__(compartments, duration, population)
assert isinstance(recovery_time, float)
assert recovery_time > ... | tutorial/source/epi_intro.ipynb | uber/pyro | apache-2.0 |
Note that we've stored data in the model. These models have a scikit-learn like interface: we instantiate a model class with data, then call a .fit_*() method to train, then call .predict() on a trained model.
Note also that we've taken special care so that t can be either an integer or a slice. Under the hood, t is an... | population = 10000
recovery_time = 10.
empty_data = [None] * 90
model = SimpleSIRModel(population, recovery_time, empty_data)
# We'll repeatedly generate data until a desired number of infections is found.
pyro.set_rng_seed(20200709)
for attempt in range(100):
synth_data = model.generate({"R0": 2.0})
total_inf... | tutorial/source/epi_intro.ipynb | uber/pyro | apache-2.0 |
The generated data contains both global variables and time series, packed into tensors. | for key, value in sorted(synth_data.items()):
print("{}.shape = {}".format(key, tuple(value.shape)))
plt.figure(figsize=(8,4))
for name, value in sorted(synth_data.items()):
if value.dim():
plt.plot(value, label=name)
plt.xlim(0, len(empty_data) - 1)
plt.ylim(0.8, None)
plt.xlabel("time step")
plt.ylab... | tutorial/source/epi_intro.ipynb | uber/pyro | apache-2.0 |
Inference <a class="anchor" id="Inference"></a>
Next let's recover estimates of the latent variables given only observations obs. To do this we'll create a new model instance from the synthetic observations. | obs = synth_data["obs"]
model = SimpleSIRModel(population, recovery_time, obs) | tutorial/source/epi_intro.ipynb | uber/pyro | apache-2.0 |
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