markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
values |
|---|---|---|---|---|
Implement Preprocess Functions
Normalize
In the cell below, implement the normalize function to take in image data, x, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as x.
图像预处理功能的实现
正规化
在如下的代码中，修改 normalize 函数，使之能够对输入的图像数据 ... | def normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (32, 32, 3)
: return: Numpy array of normalize data
"""
return x/255.0
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normali... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
One-hot encode
Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the one_hot_encode function. The input, x, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 t... | from sklearn import preprocessing
def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
# TODO: Implement Function
lb = preprocessing.LabelBinarizer()
... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Randomize Data
As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset.
随机打乱数据
正如你在上方探索数据部分所看到的，样本的顺序已经被随机打乱了。尽管再随机处理一次也没问题，不过对于该数据我们没必要再进行一次相关操作了。
Preprocess all the data and save it
Running the code cell below wi... | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode) | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Check Point
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
检查点
这是你的首个检查点。因为预处理完的数据已经被保存到硬盘上了，所以如果你需要回顾或重启该 notebook，你可以在这里重新开始。 | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper
# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb')) | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Build the network
For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittest... | import tensorflow as tf
# There are tensorflow-gpu settings, but gpu can not work becourse of the net is too big.
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))
def neural_net_image_... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Convolution and Max Pooling Layer
Convolution layers have a lot of success with images. For this code cell, you should implement the function conv2d_maxpool to apply convolution then max pooling:
* Create the weight and bias using conv_ksize, conv_num_outputs and the shape of x_tensor.
* Apply a convolution to x_tensor... | def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
"""
Apply convolution then max pooling to x_tensor
:param x_tensor: TensorFlow Tensor
:param conv_num_outputs: Number of outputs for the convolutional layer
:param conv_ksize: kernal size 2-D Tuple fo... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Flatten Layer
Implement the flatten function to change the dimension of x_tensor from a 4-D tensor to a 2-D tensor. The output should be the shape (Batch Size, Flattened Image Size). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a ch... | def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
"""
# TODO: Implement Function
return tf.contrib.layers.flatten... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Fully-Connected Layer
Implement the fully_conn function to apply a fully connected layer to x_tensor with the shape (Batch Size, num_outputs). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packag... | def fully_conn(x_tensor, num_outputs):
"""
Apply a fully connected layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_out... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Output Layer
Implement the output function to apply a fully connected layer to x_tensor with the shape (Batch Size, num_outputs). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
Note: Act... | def output(x_tensor, num_outputs):
"""
Apply a output layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Create Convolutional Model
Implement the function conv_net to create a convolutional neural network model. The function takes in a batch of images, x, and outputs logits. Use the layers you created above to create this model:
Apply 1, 2, or 3 Convolution and Max Pool layers
Apply a Flatten Layer
Apply 1, 2, or 3 Full... | def conv_net(x, keep_prob):
"""
Create a convolutional neural network model
: x: Placeholder tensor that holds image data.
: keep_prob: Placeholder tensor that hold dropout keep probability.
: return: Tensor that represents logits
"""
# TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Train the Neural Network
Single Optimization
Implement the function train_neural_network to do a single optimization. The optimization should use optimizer to optimize in session with a feed_dict of the following:
* x for image input
* y for labels
* keep_prob for keep probability for dropout
This function will be cal... | def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
"""
Optimize the session on a batch of images and labels
: session: Current TensorFlow session
: optimizer: TensorFlow optimizer function
: keep_probability: keep probability
: feature_batch: Batch of Num... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Show Stats
Implement the function print_stats to print loss and validation accuracy. Use the global variables valid_features and valid_labels to calculate validation accuracy. Use a keep probability of 1.0 to calculate the loss and validation accuracy.
显示状态
修改 print_stats 函数来打印 loss 值及验证准确率。 使用全局的变量 valid_features 及 ... | def print_stats(session, feature_batch, label_batch, cost, accuracy):
"""
Print information about loss and validation accuracy
: session: Current TensorFlow session
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
: cost: TensorFlow cost function
: accuracy... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Hyperparameters
Tune the following parameters:
* Set epochs to the number of iterations until the network stops learning or start overfitting
* Set batch_size to the highest number that your machine has memory for. Most people set them to common sizes of memory:
* 64
* 128
* 256
* ...
* Set keep_probability to the... | # TODO: Tune Parameters
epochs = 20
batch_size = 256
keep_probability = 0.5 | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Train on a Single CIFAR-10 Batch
Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section.
... | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
batch_i = 1
for batch_features, batch_label... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Fully Train the Model
Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches.
完全训练该模型
因为你在单批 CIFAR-10 数据上已经得到了一个不错的准确率了，那你可以尝试在所有五批数据上进行训练。 | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'
print('Training...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
# Loop over all batches
n_batc... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Checkpoint
The model has been saved to disk.
Test Model
Test your model against the test dataset. This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters.
检查点
该模型已经被存储到你的硬盘中。
测试模型
这部分将在测试数据集上测试你的模型。这边得到的准确率将作为你的最终准确率。你应该得到一个高于 50... | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import tensorflow as tf
import pickle
import helper
import random
# Set batch size if not already set
try:
if batch_size:
pass
except NameError:
batch_size = 64
save_model_path = './image_clas... | MachineLearning(Advanced)/p5_image_classification/image_classification_ZH-CN.ipynb | StudyExchange/Udacity | mit |
Elements Are Lists | root.tag
len(root)
for child in root:
print(child) | learn_stem/python/dive-into-python-xml.ipynb | wgong/open_source_learning | apache-2.0 |
Attributes Are Dictonaries | root.attrib
c4_att = root[4].attrib
c4_att
c4_att['rel'],c4_att['href'] | learn_stem/python/dive-into-python-xml.ipynb | wgong/open_source_learning | apache-2.0 |
Searching | # find 1st matching entry
tree.find('//{http://www.w3.org/2005/Atom}entry')
# find all entry elements
tree.findall('//{http://www.w3.org/2005/Atom}entry')
# find all category elements
tree.findall('//{http://www.w3.org/2005/Atom}category')
# find all category element with attribute term="mp4"
tree.findall('//{http:/... | learn_stem/python/dive-into-python-xml.ipynb | wgong/open_source_learning | apache-2.0 |
Generating XML | new_feed = etree.Element('{http://www.w3.org/2005/Atom}feed',
attrib={'{http://www.w3.org/XML/1998/namespace}lang': 'en'})
print(etree.tostring(new_feed))
# add more element/text
title = etree.SubElement(new_feed, 'title', attrib={'type':'html'})
print(etree.tounicode(new_feed))
title.text = 'Dive into Pyth... | learn_stem/python/dive-into-python-xml.ipynb | wgong/open_source_learning | apache-2.0 |
Note that the more global package <i>docplex</i> contains another subpackage <i>docplex.mp</i> that is dedicated to Mathematical Programming, another branch of optimization.
Step 2: Model the data
Next section defines the data of the problem. | from docplex.cp.model import *
# List of possible truck configurations. Each tuple is (load, cost) with:
# load: max truck load for this configuration,
# cost: cost for loading the truck in this configuration
TRUCK_CONFIGURATIONS = ((11, 2), (11, 2), (11, 2), (11, 3), (10, 3), (10, 3), (10, 4))
# List of custom... | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
Step 3: Set up the prescriptive model
Prepare data for modeling
Next section extracts from problem data the parts that are frequently used in the modeling section. | nbTruckConfigs = len(TRUCK_CONFIGURATIONS)
maxTruckConfigLoad = [tc[0] for tc in TRUCK_CONFIGURATIONS]
truckCost = [tc[1] for tc in TRUCK_CONFIGURATIONS]
maxLoad = max(maxTruckConfigLoad)
nbOrders = len(CUSTOMER_ORDERS)
nbCustomers = 1 + max(co[0] for co in CUSTOMER_ORDERS)
volumes = [co[1] for co in CUSTOMER_ORDERS]
... | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
Create CPO model | mdl = CpoModel(name="trucks") | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
Define the decision variables | # Configuration of the truck for each delivery
truckConfigs = integer_var_list(maxDeliveries, 0, nbTruckConfigs - 1, "truckConfigs")
# In which delivery is an order
where = integer_var_list(nbOrders, 0, maxDeliveries - 1, "where")
# Load of a truck
load = integer_var_list(maxDeliveries, 0, maxLoad, "load")
# Number of ... | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
Express the business constraints | # transitionCost[i] = transition cost between configurations i and i+1
for i in range(1, maxDeliveries):
auxVars = (truckConfigs[i - 1], truckConfigs[i], transitionCost[i - 1])
mdl.add(allowed_assignments(auxVars, CONFIGURATION_TRANSITION_COST))
# Constrain the volume of the orders in each truck
mdl.add(pack(l... | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
Express the objective | # Objective: first criterion for minimizing the cost for configuring and loading trucks
# second criterion for minimizing the number of deliveries
cost = sum(transitionCost) + sum(element(truckConfigs[i], truckCost) * (load[i] != 0) for i in range(maxDeliveries))
mdl.add(minimize_static_lex([cost, nbDeliver... | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
Solve with Decision Optimization solve service | # Search strategy: first assign order to truck
mdl.set_search_phases([search_phase(where)])
# Solve model
print("\nSolving model....")
msol = mdl.solve(TimeLimit=20) | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
Step 4: Investigate the solution and then run an example analysis | if msol.is_solution():
print("Solution: ")
ovals = msol.get_objective_values()
print(" Configuration cost: {}, number of deliveries: {}".format(ovals[0], ovals[1]))
for i in range(maxDeliveries):
ld = msol.get_value(load[i])
if ld > 0:
stdout.write(" Delivery {:2d}: confi... | examples/cp/jupyter/truck_fleet.ipynb | IBMDecisionOptimization/docplex-examples | apache-2.0 |
This time we are not going to generate the data but rather use real world annotated training examples. | # Dataset creation
import numpy as np
import math
import random
import csv
from neon.datasets.dataset import Dataset
class WorkoutDS(Dataset):
# Number of features per example
feature_count = None
# Number of examples
num_train_examples = None
num_test_examples = None
# Number of classes
... | ipython-analysis/exercise-cnn.ipynb | datastax-demos/Muvr-Analytics | bsd-3-clause |
At first we want to inspect the class distribution of the training and test examples. | from ipy_table import *
from operator import itemgetter
import numpy as np
train_dist = np.reshape(np.transpose(np.sum(dataset.y_train, axis=0)), (dataset.num_labels,1))
test_dist = np.reshape(np.transpose(np.sum(dataset.y_test, axis=0)), (dataset.num_labels,1))
train_ratio = train_dist / dataset.num_train_examples
t... | ipython-analysis/exercise-cnn.ipynb | datastax-demos/Muvr-Analytics | bsd-3-clause |
Let's have a look at the generated data. We will plot some of the examples of the different classes. | from matplotlib import pyplot, cm
from pylab import *
# Choose some random examples to plot from the training data
number_of_examples_to_plot = 3
plot_ids = np.random.random_integers(0, dataset.num_train_examples - 1, number_of_examples_to_plot)
print "Ids of plotted examples:",plot_ids
# Retrieve a human readable l... | ipython-analysis/exercise-cnn.ipynb | datastax-demos/Muvr-Analytics | bsd-3-clause |
Now we are going to create a neon model. We will start with a realy simple one layer preceptron having 500 hidden units. | from neon.backends import gen_backend
from neon.layers import *
from neon.models import MLP
from neon.transforms import RectLin, Tanh, Logistic, CrossEntropy
from neon.experiments import FitPredictErrorExperiment
from neon.params import val_init
from neon.util.persist import serialize
# General settings
max_epochs = 7... | ipython-analysis/exercise-cnn.ipynb | datastax-demos/Muvr-Analytics | bsd-3-clause |
To check weather the network is learning something we will plot the weight matrices of the different training epochs. | import numpy as np
import math
from matplotlib import pyplot, cm
from pylab import *
from IPython.html import widgets
from IPython.html.widgets import interact
def closestSqrt(i):
N = int(math.sqrt(i))
while True:
M = int(i / N)
if N * M == i:
return N, M
N -= 1
def... | ipython-analysis/exercise-cnn.ipynb | datastax-demos/Muvr-Analytics | bsd-3-clause |
Let's also have a look at the confusion matrix for the test dataset. | from sklearn.metrics import confusion_matrix
from ipy_table import *
# confusion_matrix(y_true, y_pred)
predicted, actual = model.predict_fullset(dataset, "test")
y_pred = np.argmax(predicted.asnumpyarray(), axis = 0)
y_true = np.argmax(actual.asnumpyarray(), axis = 0)
confusion_mat = confusion_matrix(y_true, y_pr... | ipython-analysis/exercise-cnn.ipynb | datastax-demos/Muvr-Analytics | bsd-3-clause |
ARIMA Example 1: Arima
As can be seen in the graphs from Example 2, the Wholesale price index (WPI) is growing over time (i.e. is not stationary). Therefore an ARMA model is not a good specification. In this first example, we consider a model where the original time series is assumed to be integrated of order 1, so tha... | # Dataset
wpi1 = requests.get('http://www.stata-press.com/data/r12/wpi1.dta').content
data = pd.read_stata(BytesIO(wpi1))
data.index = data.t
# Fit the model
mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,1))
res = mod.fit()
print(res.summary()) | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
Thus the maximum likelihood estimates imply that for the process above, we have:
$$
\Delta y_t = 0.1050 + 0.8740 \Delta y_{t-1} - 0.4206 \epsilon_{t-1} + \epsilon_{t}
$$
where $\epsilon_{t} \sim N(0, 0.5226)$. Finally, recall that $c = (1 - \phi_1) \beta_0$, and here $c = 0.1050$ and $\phi_1 = 0.8740$. To compare with ... | # Dataset
data = pd.read_stata(BytesIO(wpi1))
data.index = data.t
data['ln_wpi'] = np.log(data['wpi'])
data['D.ln_wpi'] = data['ln_wpi'].diff()
# Graph data
fig, axes = plt.subplots(1, 2, figsize=(15,4))
# Levels
axes[0].plot(data.index._mpl_repr(), data['wpi'], '-')
axes[0].set(title='US Wholesale Price Index')
# L... | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
To understand how to specify this model in Statsmodels, first recall that from example 1 we used the following code to specify the ARIMA(1,1,1) model:
python
mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,1))
The order argument is a tuple of the form (AR specification, Integration order, MA specific... | # Fit the model
mod = sm.tsa.statespace.SARIMAX(data['ln_wpi'], trend='c', order=(1,1,1))
res = mod.fit()
print(res.summary()) | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
ARIMA Example 3: Airline Model
In the previous example, we included a seasonal effect in an additive way, meaning that we added a term allowing the process to depend on the 4th MA lag. It may be instead that we want to model a seasonal effect in a multiplicative way. We often write the model then as an ARIMA $(p,d,q) \... | # Dataset
air2 = requests.get('http://www.stata-press.com/data/r12/air2.dta').content
data = pd.read_stata(BytesIO(air2))
data.index = pd.date_range(start=datetime(data.time[0], 1, 1), periods=len(data), freq='MS')
data['lnair'] = np.log(data['air'])
# Fit the model
mod = sm.tsa.statespace.SARIMAX(data['lnair'], order... | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
Notice that here we used an additional argument simple_differencing=True. This controls how the order of integration is handled in ARIMA models. If simple_differencing=True, then the time series provided as endog is literatlly differenced and an ARMA model is fit to the resulting new time series. This implies that a nu... | # Dataset
friedman2 = requests.get('http://www.stata-press.com/data/r12/friedman2.dta').content
data = pd.read_stata(BytesIO(friedman2))
data.index = data.time
# Variables
endog = data.ix['1959':'1981', 'consump']
exog = sm.add_constant(data.ix['1959':'1981', 'm2'])
# Fit the model
mod = sm.tsa.statespace.SARIMAX(end... | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
ARIMA Postestimation: Example 1 - Dynamic Forecasting
Here we describe some of the post-estimation capabilities of Statsmodels' SARIMAX.
First, using the model from example, we estimate the parameters using data that excludes the last few observations (this is a little artificial as an example, but it allows considerin... | # Dataset
raw = pd.read_stata(BytesIO(friedman2))
raw.index = raw.time
data = raw.ix[:'1981']
# Variables
endog = data.ix['1959':, 'consump']
exog = sm.add_constant(data.ix['1959':, 'm2'])
nobs = endog.shape[0]
# Fit the model
mod = sm.tsa.statespace.SARIMAX(endog.ix[:'1978-01-01'], exog=exog.ix[:'1978-01-01'], order... | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
We can graph the one-step-ahead and dynamic predictions (and the corresponding confidence intervals) to see their relative performance. Notice that up to the point where dynamic prediction begins (1978:Q1), the two are the same. | # Graph
fig, ax = plt.subplots(figsize=(9,4))
npre = 4
ax.set(title='Personal consumption', xlabel='Date', ylabel='Billions of dollars')
# Plot data points
data.ix['1977-07-01':, 'consump'].plot(ax=ax, style='o', label='Observed')
# Plot predictions
predict.predicted_mean.ix['1977-07-01':].plot(ax=ax, style='r--', la... | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
Finally, graph the prediction error. It is obvious that, as one would suspect, one-step-ahead prediction is considerably better. | # Prediction error
# Graph
fig, ax = plt.subplots(figsize=(9,4))
npre = 4
ax.set(title='Forecast error', xlabel='Date', ylabel='Forecast - Actual')
# In-sample one-step-ahead predictions and 95% confidence intervals
predict_error = predict.predicted_mean - endog
predict_error.ix['1977-10-01':].plot(ax=ax, label='One-... | examples/notebooks/statespace_sarimax_stata.ipynb | wzbozon/statsmodels | bsd-3-clause |
The following function will not work with the original VM for the Short course. To use, install s3fs package (via conda or pip). It is a file system interface for AWS S3 buckets and provides a nice interface similar to unix/ftp command line arguments. | def open_nexrad_file(filename, io='radx'):
"""
Open file using pyart nexrad archive method.
Parameters
----------
filename: str
Radar filename to open.
io: str
Py-ART open method. If radx then file is opened via Radx
otherwise via native Py-ART function.
Using Ra... | 5b_PyART_visualization.ipynb | gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | bsd-2-clause |
Py-ART Colormaps
Retrieve the names of colormaps and the colormap list dictionary.
The colormaps are registered with matplotlib and can be accessed by inserting 'pyart_' in front of any name. | cm_names = pyart.graph.cm._cmapnames
cms = pyart.graph.cm.cmap_d
nrows = len(cm_names)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
# Create a figure and axes instance
fig, axes = plt.subplots(nrows=nrows, figsize=(5,10))
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)... | 5b_PyART_visualization.ipynb | gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | bsd-2-clause |
The RadarDisplay
This is the most commonly used class designed for surface-based scanning radar
Plot a NEXRAD file | nexf = "data/KILN20140429_231254_V06"
nexr = pyart.io.read(nexf)
nexd = pyart.graph.RadarDisplay(nexr)
nexr.fields.keys()
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(16, 12))
nexd.plot('reflectivity', sweep=1, cmap='pyart_NWSRef', vmin=0., vmax=55., mask_outside=False, ax=ax[0, 0])
nexd.plot_range_rings([50, 10... | 5b_PyART_visualization.ipynb | gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | bsd-2-clause |
There are many keyword values we can employe to refine the plot
Keywords exist for title, labels, colorbar, along with others.
In addition, there are many methods that can be employed. For example, pull out a constructed RHI at a given azimuth. | nexd.plot_azimuth_to_rhi('reflectivity', 305., cmap='pyart_NWSRef', vmin=0., vmax=55.)
nexd.set_limits((0., 150.), (0., 15.)) | 5b_PyART_visualization.ipynb | gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | bsd-2-clause |
Py-ART RHI
Not only can we construct an RHI from a PPI volume, but RHI scans may be plotted as well. | rhif = "data/noxp_rhi_140610232635.RAWHJFH"
rhir = pyart.io.read(rhif)
rhid = pyart.graph.RadarDisplay(rhir)
rhid.plot_rhi('reflectivity', 0, vmin=-5.0, vmax=70,)
rhid.set_limits(xlim=(0, 50), ylim=(0, 15)) | 5b_PyART_visualization.ipynb | gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | bsd-2-clause |
Py-ART RadarMapDisplay or RadarMapDisplayCartopy
This converts the x-y coordinates to latitude and longitude overplotting on a map
Let us see what version we have. The first is works on Py-ART which uses a standard definition. For other packages that may not the second method should work | pyart_ver = pyart.__version__
import pkg_resources
pyart_ver2 = pkg_resources.get_distribution("arm_pyart").version
if int(pyart_ver.split('.')[1]) == 8:
print("8")
nexmap = pyart.graph.RadarMapDisplayCartopy(nexr)
else:
print("7")
nexmap = pyart.graph.RadarMapDisplay(nexr)
limits = [-87., -82., 37.,... | 5b_PyART_visualization.ipynb | gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | bsd-2-clause |
Use what you have learned!
Using all that you have learned, make a two panel plot of reflectivity and doppler velocity using the data file from an RHI of NOXP data/noxp_rhi_140610232635.RAWHJFH. Use Cartopy to overlay the plots on a map of Austrailia and play around with differing colormaps and axes limits!
Solution
T... | # %load solution.py | 5b_PyART_visualization.ipynb | gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | bsd-2-clause |
Author: Thomas Cokelaer
Jan 2018
Local time execution: about 10 minutes
In this notebook, we will simulate fastq reads and inject CNVs. We will then look at the sensitivity (proportion of true positive by the sum of positives) of sequana_coverage.
We use the data and strategy described in section 3.2 of "CNOGpro: det... | !sequana_coverage --download-reference FN433596 | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Simulated FastQ data
Installation: conda install art
Simulation of data coverage 100X
-l: length of the reads
-f: coverage
-m: mean size of fragments
-s: standard deviation of fragment size
-ss: type of hiseq
This takes a few minutes to produce | ! art_illumina -sam -i FN433596.fa -p -l 100 -ss HS20 -f 20 -m 500 -s 40 -o paired_dat -f 100 | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Creating the BAM (mapping) and BED files | # no need for the *aln and *sam, let us remove them to save space
!rm -f paired*.aln paired_dat.sam
!sequana_mapping --reference FN433596.fa --file1 paired_dat1.fq --file2 paired_dat2.fq 1>out 2>err | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
This uses bwa and samtools behind the scene. Then, we will convert the resulting BAM file (FN433596.fasta.sorted.bam) into a BED file once for all. To do so, we use bioconvert (http://bioconvert.readthedocs.io) that uses bedtools behind the scene: | # bioconvert FN433596.fa.sorted.bam simulated.bed -f
# or use e.g. bedtools:
!bedtools genomecov -d -ibam FN433596.fa.sorted.bam > simulated.bed | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
sequana_coverage
We execute sequana_coverage to find the ROI (region of interest). We should a few detections (depends on the threshold and length of the genome of course).
Later, we will inject events as long as 8000 bases. So, we should use at least 16000 bases for the window parameter length. As shown in the window... | !sequana_coverage --input simulated.bed --reference FN433596.fa -w 20001 -o --level WARNING -C .5
!cp report/*/*/rois.csv rois_noise_20001.csv
# An instance of coverage signal (yours may be slightly different)
from IPython.display import Image
Image("coverage.png") | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
The false positives | %pylab inline
# Here is a convenient function to plot the ROIs in terms of sizes
# and max zscore
def plot_results(file_roi, choice="max"):
import pandas as pd
roi = pd.read_csv(file_roi) #"rois_cnv_deletion.csv")
roi = roi.query("start>100 and end<3043210")
plot(roi["size"], roi["{}_zscore".format(ch... | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Most of the detected events have a zscore close to the chosen thresholds (-4 and 4). Moreover,
most events have a size below 50.
So for the detection of CNVs with size above let us say 2000, the False positives is (FP = 0).
More simulations would be required to get a more precise idea of the FP for short CNVs but the... | import random
import pandas as pd
def create_deletion():
df = pd.read_csv("simulated.bed", sep="\t", header=None)
positions = []
sizes = []
for i in range(80):
# the + and -4000 shift are there to guarantee the next
# CNV does not overlap with the previous one since
# CNV length... | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Deleted regions are all detected | # call this only once !!!!
positions_deletion, sizes_deletion = create_deletion()
!sequana_coverage --input cnv_deletion.bed -o -w 20001 --level WARNING
!cp report/*/*/rois.csv rois_cnv_deleted.csv
rois_deletion = plot_results("rois_cnv_deleted.csv")
# as precise as 2 base positions but for safety, we put precision ... | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
duplicated regions | positions_duplicated, sizes_duplicated = create_duplicated()
!sequana_coverage --input cnv_duplicated.bed -o -w 40001 --level ERROR -C .3 --no-html --no-multiqc
!cp report/*/*/rois.csv rois_cnv_duplicated_40001.csv
rois_duplicated = plot_results("rois_cnv_duplicated_40001.csv", choice="max") | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Same results with W=20000,40000,60000,100000 but recovered CN is better
with larger W | rois_duplicated = plot_results("rois_cnv_duplicated_20000.csv", choice="max") | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Note that you may see events with negative zscore. Those are false detection due to the presence of two CNVs close to each other. This can be avoided by increasing the window size e.g. to 40000 | check_found(positions_duplicated, sizes_duplicated, rois_duplicated,
precision=5) | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Mixes of duplicated and deleted regions | positions_mix, sizes_mix = create_cnvs_mixed()
!sequana_coverage --input cnv_mixed.bed -o -w 40001 --level ERROR --no-multiqc --no-html --cnv-clustering 1000
!cp report/*/*/rois.csv rois_cnv_mixed.csv
Image("coverage_with_cnvs.png")
rois_mixed = plot_results("rois_cnv_mixed.csv", choice="max")
# note that here we ... | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Some events (about 1%) may be labelled as not found but visual inspection will show that there are actually detected. This is due to a starting position being offset due to noise data set that interfer with the injected CNVs.
Conclusions
with simulated data and no CNV injections, sequana coverage detects some events t... | roi = plot_results("rois_noise_20001.csv")
what is happening here is that we detect many events close to the threshold.
So for instance all short events on the left hand side have z-score close to 4,
which is our threshold.
By pure chance, we get longer events of 40 or 50bp. This is quite surprinsing and wanted to... | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
With 50 simulations, we get 826 events. (100 are removed because on the edge of the origin of replication), which means about 16 events per simulation. The max length is 90.
None of the long events (above 50) appear at the same position (distance by more than 500 bases at least) so long events are genuine false positi... | roi = plot_results("100_simulated_rois.csv", choice="mean")
roi = plot_results("100_simulated_rois.csv", choice="max") | coverage/05-sensitivity/sensitivity.ipynb | sequana/resources | bsd-3-clause |
Check for missing data | #Checking for missing data
NAs = pd.concat([train.isnull().sum(), test.isnull().sum()], axis=1, keys=['Train', 'Test'])
NAs[NAs.sum(axis=1) > 0] | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Helper functions | # Prints R2 and RMSE scores
def get_score(prediction, lables):
print('R2: {}'.format(r2_score(prediction, lables)))
print('RMSE: {}'.format(np.sqrt(mean_squared_error(prediction, lables))))
# Shows scores for train and validation sets
def train_test(estimator, x_trn, x_tst, y_trn, y_tst):
predictio... | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Removing outliers | sns.lmplot(x='GrLivArea', y='SalePrice', data=train)
train = train[train.GrLivArea < 4500]
sns.lmplot(x='GrLivArea', y='SalePrice', data=train) | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Splitting to features and labels and deleting variables I don't need | # Spliting to features and lables
train_labels = train.pop('SalePrice')
features = pd.concat([train, test], keys=['train', 'test'])
# Deleting features that are more than 50% missing
features.drop(['PoolQC', 'MiscFeature', 'FireplaceQu', 'Fence', 'Alley'],
axis=1, inplace=True)
features.shape | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Filling missing values | # MSZoning NA in pred. filling with most popular values
features['MSZoning'] = features['MSZoning'].fillna(features['MSZoning'].mode()[0])
# LotFrontage NA in all. I suppose NA means 0
features['LotFrontage'] = features['LotFrontage'].fillna(features['LotFrontage'].mean())
# MasVnrType NA in all. filling with most p... | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Log transformation | # Our SalesPrice is skewed right (check plot below). I'm logtransforming it.
ax = sns.distplot(train_labels)
## Log transformation of labels
train_labels = np.log(train_labels)
## Now it looks much better
ax = sns.distplot(train_labels) | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Converting categorical features with order to numerical
Converting categorical variables with choices: Ex, Gd, TA, FA and Po
def cat2numCondition(x):
if x == 'Ex':
return 5
if x == 'Gd':
return 4
if x == 'TA':
return 3
if x == 'Fa':
return 2
if x == 'Po':
retu... | def num2cat(x):
return str(x)
features['MSSubClass_str'] = features['MSSubClass'].apply(num2cat)
features.pop('MSSubClass')
features.shape | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Converting categorical features to binary | # Getting Dummies from all other categorical vars
for col in features.dtypes[features.dtypes == 'object'].index:
for_dummy = features.pop(col)
features = pd.concat([features, pd.get_dummies(for_dummy, prefix=col)], axis=1)
features.shape
features.head() | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Overfitting columns | #features.drop('MSZoning_C (all)',axis=1) | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Splitting train and test features | ### Splitting features
train_features = features.loc['train'].drop('Id', axis=1).select_dtypes(include=[np.number]).values
test_features = features.loc['test'].drop('Id', axis=1).select_dtypes(include=[np.number]).values | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Splitting to train and validation sets | ### Splitting
x_train, x_test, y_train, y_test = train_test_split(train_features,
train_labels,
test_size=0.1,
random_state=200) | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Modeling
1. Gradient Boosting Regressor | GBR = GradientBoostingRegressor(n_estimators=12000,
learning_rate=0.05, max_depth=3, max_features='sqrt',
min_samples_leaf=15, min_samples_split=10, loss='huber')
GBR.fit(x_train, y_train)
train_test(GBR, x_train, x_test, y_train, y_test)
# Average R2 score and standart deviation of 5-fold cr... | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
2. LASSO regression | lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))
lasso.fit(x_train, y_train)
train_test(lasso, x_train, x_test, y_train, y_test)
# Average R2 score and standart deviation of 5-fold cross-validation
scores = cross_val_score(lasso, train_features, train_labels, cv=5)
print("Accuracy: %0.2f (... | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
3. Elastic Net Regression | ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))
ENet.fit(x_train, y_train)
train_test(ENet, x_train, x_test, y_train, y_test)
# Average R2 score and standart deviation of 5-fold cross-validation
scores = cross_val_score(ENet, train_features, train_labels, cv=5)
print("Accu... | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Averaging models | # Retraining models on all train data
GBR.fit(train_features, train_labels)
lasso.fit(train_features, train_labels)
ENet.fit(train_features, train_labels)
def averaginModels(X, train, labels, models=[]):
for model in models:
model.fit(train, labels)
predictions = np.column_stack([
model.predict... | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Submission | test_id = test.Id
test_submit = pd.DataFrame({'Id': test_id, 'SalePrice': test_y})
test_submit.shape
test_submit.head()
test_submit.to_csv('house_price_pred_avg_gbr_lasso_enet.csv', index=False) | Script/SKlearn models.ipynb | maviator/Kaggle_home_price_prediction | mit |
Testing the median filter with a fixed window length of 16. | median = plt.figure(figsize=(30,20))
for x in range(1, 5):
for y in range(1, 6):
plt.subplot(5, 5, x + (y-1)*4)
wavenum = (x-1) + (y-1)*4
functions.medianSinPlot(wavenum, 15)
plt.suptitle('Median filtered Sine Waves with window length 15', fontsize = 60)
plt.xlabel(("Wave num... | MedianFilter/Python/01. Basic Tests Median Filter/basic median filter with window length 16.ipynb | ktakagaki/kt-2015-DSPHandsOn | gpl-2.0 |
Summary
with higher wave numbers (n=10), the filter makes the signal even worse with a phase (amplitude) reversal!
a bit of ailiasing, would benefit from more sample points
Graphic Export | pp=PdfPages( 'median sin window length 15.pdf' )
pp.savefig( median )
pp.close() | MedianFilter/Python/01. Basic Tests Median Filter/basic median filter with window length 16.ipynb | ktakagaki/kt-2015-DSPHandsOn | gpl-2.0 |
Compressed ACL
Now, assume that we want to compress this ACL to make it more manageable. We do the following operations:
Merge the two BFD permit statements on lines 20-30 into one statement using the range directive.
Remove the BGP session on line 80 because it has been decommissioned
Remove lines 180 and 250 because... | compressed_acl = """
ip access-list acl
10 deny icmp any any redirect
20 permit udp 117.186.185.0/24 range 49152 65535 117.186.185.0/24 range 3784 3785
! 30 MERGED WITH LINE ABOVE
40 permit tcp 11.36.216.170/32 11.36.216.169/32 eq bgp
50 permit tcp 11.36.216.176/32 11.36.216.179/32 eq bgp
60 permit tcp 204.1... | jupyter_notebooks/Safely refactoring ACLs and firewall rules.ipynb | batfish/pybatfish | apache-2.0 |
The challenge for us is to find out if and how this compressed ACL differs from the original. That is, is there is traffic that is treated differently by the two ACLs, and if so, which lines are responsible for the difference.
This task is difficult to get right through manual reasoning alone, which is why we developed... | # Import packages
%run startup.py
bf = Session(host="localhost")
# Initialize a snapshot with the original ACL
original_snapshot = bf.init_snapshot_from_text(
original_acl,
platform="cisco-nx",
snapshot_name="original",
overwrite=True)
# Initialize a snapshot with the compressed ACL
compressed_sna... | jupyter_notebooks/Safely refactoring ACLs and firewall rules.ipynb | batfish/pybatfish | apache-2.0 |
The compareFilters question compares two filters and returns pairs of lines, one from each filter, that match the same flow(s) but treat them differently. If it reports no output, the filters are guaranteed to be identical. The analysis is exhaustive and considers all possible flows.
As we can see from the output above... | smaller_acls = """
ip access-list deny-icmp-redirect
10 deny icmp any any redirect
ip access-list permit-bfd
20 permit udp 117.186.185.0/24 range 49152 65535 117.186.185.0/24 range 3784 3785
ip access-list permit-bgp-session
40 permit tcp 11.36.216.170/32 11.36.216.169/32 eq bgp
50 permit tcp 11.36.216.176/32... | jupyter_notebooks/Safely refactoring ACLs and firewall rules.ipynb | batfish/pybatfish | apache-2.0 |
Given the split ACLs above, one analysis may be to figure out if each untrusted source subnet was included in a smaller ACL. Otherwise, we have lost protection that was present in the original ACL. We can accomplish this analysis via the findMatchingFilterLines question, as shown below.
Once we are satisfied with anal... | # Initialize a snapshot with the smaller ACLs
smaller_snapshot = bf.init_snapshot_from_text(
smaller_acls,
platform="cisco-nx",
snapshot_name="smaller",
overwrite=True)
# All untrusted subnets
untrusted_source_subnets = ["54.0.0.0/8",
"163.157.0.0/16",
... | jupyter_notebooks/Safely refactoring ACLs and firewall rules.ipynb | batfish/pybatfish | apache-2.0 |
<h2> Create ML dataset by sampling using BigQuery </h2>
<p>
Sample the BigQuery table publicdata.samples.natality to create a smaller dataset of approximately 10,000 training and 3,000 evaluation records. Restrict your samples to data after the year 2000.
</p> | # TODO | quests/endtoendml/labs/2_sample.ipynb | turbomanage/training-data-analyst | apache-2.0 |
Preprocess data using Pandas
Carry out the following preprocessing operations:
Add extra rows to simulate the lack of ultrasound.
Change the plurality column to be one of the following strings:
<pre>
['Single(1)', 'Twins(2)', 'Triplets(3)', 'Quadruplets(4)', 'Quintuplets(5)']
</pre>
Remove rows where any of the imp... | ## TODO | quests/endtoendml/labs/2_sample.ipynb | turbomanage/training-data-analyst | apache-2.0 |
<h2> Write out </h2>
<p>
In the final versions, we want to read from files, not Pandas dataframes. So, write the Pandas dataframes out as CSV files.
Using CSV files gives us the advantage of shuffling during read. This is important for distributed training because some workers might be slower than others, and shufflin... | traindf.to_csv('train.csv', index=False, header=False)
evaldf.to_csv('eval.csv', index=False, header=False)
%%bash
wc -l *.csv
head *.csv
tail *.csv | quests/endtoendml/labs/2_sample.ipynb | turbomanage/training-data-analyst | apache-2.0 |
Si llamamos a la función una vez... | funcion() | More/DefaultParametersInPython_ES.ipynb | aaossa/Dear-Notebooks | gpl-3.0 |
... todo funciona como lo suponemos, pero y si probamos otra vez... | funcion()
funcion() | More/DefaultParametersInPython_ES.ipynb | aaossa/Dear-Notebooks | gpl-3.0 |
... ok? No funciona como lo supondriamos.
Esto también podemos extenderlo a clases, donde es comun usar parámetros por defecto: | class Clase:
def __init__(self, lista=[]):
self.lista = lista
self.lista.append(1)
print("Lista de la clase: {}".format(self.lista))
# Instanciamos dos objetos
A = Clase()
B = Clase()
# Modificamos el parametro en una
A.lista.append(5)
# What??
print(A.lista)
print(B.lista) | More/DefaultParametersInPython_ES.ipynb | aaossa/Dear-Notebooks | gpl-3.0 |
Investigando nuestro código
Veamos un poco qué está pasando en nuestro código: | # Instanciemos algunos objetos
A = Clase()
B = Clase()
C = Clase(lista=["GG"]) # Usaremos esta isntancia como control
print("\nLos objetos son distintos!")
print("id(A): {} \nid(B): {} \nid(C): {}".format(id(A), id(B), id(C)))
print("\nPero la lista es la misma para A y para B :O")
print("id(A.lista): {} \nid(B.lista... | More/DefaultParametersInPython_ES.ipynb | aaossa/Dear-Notebooks | gpl-3.0 |
¿Qué está pasando? D:
En Python, las funciones son objetos del tipo callable, es decir, que son llamables, ejecutan una operación. | # De hecho, tienen atributos...
def funcion(lista=[]):
lista.append(5)
# En la funcion "funcion"...
print("{}".format(funcion.__defaults__))
# ... si la invocamos...
funcion()
# ahora tenemos...
print("{}".format(funcion.__defaults__))
# Si vemos como quedo el metodo "__init__" de la clase Clase...
print(... | More/DefaultParametersInPython_ES.ipynb | aaossa/Dear-Notebooks | gpl-3.0 |
El código que define a función es evaluado una vez y dicho valor evaluado es el que se usa en cada llamado posterior. Por lo tanto, al modificar el valor de un parámetro por defecto que es mutable (list, dict, etc.) se modifica el valor por defecto para el siguiente llamado.
¿Cómo evitar esto?
Una solución simple es us... | class Clase:
def __init__(self, lista=None):
# Version "one-liner":
self.lista = lista if lista is not None else list()
# En su version extendida:
if lista is not None:
self.lista = lista
else:
self.lista = list() | More/DefaultParametersInPython_ES.ipynb | aaossa/Dear-Notebooks | gpl-3.0 |
Pick one of these to explore re: below models | # Look only at train IDs
features = df.columns.values
X = train_id_dummies
y = df['ord_del']
# Non Delay Specific
features = df.columns.values
target_cols = ['temp','precipiation',
'visability','windspeed','humidity','cloudcover',
'is_bullet','is_limited','t_northbound',
'd_monday','d_tuesday','... | 06initial_analysis.ipynb | readywater/caltrain-predict | mit |
Run Decision Trees, Prune, and consider False Positives | from sklearn.tree import DecisionTreeClassifier
TreeClass = DecisionTreeClassifier(
max_depth = 2,
min_samples_leaf = 5)
TreeClass.fit(X,y)
from sklearn.cross_validation import cross_val_score
scores = cross_val_score(TreeClass, X, y, cv=10)
print(scores.mean()) # Score = More is better... | 06initial_analysis.ipynb | readywater/caltrain-predict | mit |
As a check, consider Feature selection | from sklearn import feature_selection
pvals = feature_selection.f_regression(X,y)[1]
sorted(zip(X.columns.values,np.round(pvals,4)),key=lambda x:x[1],reverse=True)
X_lr=df[['windspeed','t_northbound','precipiation','d_friday']]
# localize your search around the maximum value you found
c_list = np.logspace(-1,1,21)
c... | 06initial_analysis.ipynb | readywater/caltrain-predict | mit |
Find the Principal Components | X = only_delay[['temp','precipiation',
'visability','windspeed','humidity','cloudcover',
'is_bullet','is_limited','t_northbound',
'd_monday','d_tuesday','d_wednesday','d_thursday','d_friday','d_saturday']]
from sklearn.decomposition import PCA
clf = PCA(.99)
X_trans = clf.fit_transform(X)
X_tran... | 06initial_analysis.ipynb | readywater/caltrain-predict | mit |
Seeing if I can get anything interesting out of KNN given above
Lecture 10, look at Confusion matrix and ROC curve. Fiddle with the thresholds and AUC | print df['windspeed'].max()
print df['windspeed'].min()
df['windspeed_st'] = df['windspeed'].apply(lambda x:x/15.0) # Ballparking
X_reg = df[['precipiation','d_friday','t_northbound','windspeed_st']]
y_reg = df['is_delay']
from sklearn import cross_validation
from sklearn import neighbors, metrics
kf = cross_valid... | 06initial_analysis.ipynb | readywater/caltrain-predict | mit |
Cross Validation and Random Forest | from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_score
RFClass = RandomForestClassifier(n_estimators = 10000,
max_features = 4, # You can set it to a number or 'sqrt', 'log2', etc
min_samples_leaf = 5,
... | 06initial_analysis.ipynb | readywater/caltrain-predict | mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.