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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Add a column | responses=househld[['REGION','WTFA_HH']].groupby('REGION').count()
responses.name = "Responses"
by_region['Responses']=responses
by_region | notebooks/Explore_Files.ipynb | gsentveld/lunch_and_learn | mit |
And we will change the index to a more complex one, based on the documentation of the household file. | by_region.index=['Northeast','Midwest','South','West']
by_region | notebooks/Explore_Files.ipynb | gsentveld/lunch_and_learn | mit |
Saving this result
We can use any of the to_xyz() functions to save this data to a file.
Here we don't supply a path to save the data, which in turn just returns the result in the requested format. | print(by_region.to_json()) | notebooks/Explore_Files.ipynb | gsentveld/lunch_and_learn | mit |
Dealing with missing values
It appears that the household file also holds information about why people did not respond. This field is empty if people responded.
We are going to use that to filter the data, with a boolean index.
We will use the NON_INTV response code to create the boolean index | non_response_code=househld['NON_INTV']
import math
# If the value Is Not A Number math.isnan() will return True.
responded=[math.isnan(x) for x in non_response_code]
notresponded=[not math.isnan(x) for x in non_response_code]
resp=househld[responded]
nonresp=househld[notresponded]
print("Total size: {}".format(hou... | notebooks/Explore_Files.ipynb | gsentveld/lunch_and_learn | mit |
Now we create a group by the reason code, why people did not respond | non_intv_group=nonresp.groupby('NON_INTV')
non_intv_group.size() | notebooks/Explore_Files.ipynb | gsentveld/lunch_and_learn | mit |
Filling missing data
If we just plot the data from the original DataFrame, we only get the data with a value.
We can use the fillna() function to solve that and see all data. | househld['INTV_MON'].hist(by=househld['NON_INTV'].fillna(0)) | notebooks/Explore_Files.ipynb | gsentveld/lunch_and_learn | mit |
Neural nets
All nets inherit from sklearn.BaseEstimator and have the same interface as another wrappers in REP (details see in 01-howto-Classifiers)
All of these nets libraries support:
classification
multi-classification
regression
multi-target regresssion
additional fitting (using partial_fit method)
and don't supp... | variables = list(data.columns[:25]) | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Simple training | tn = TheanetsClassifier(features=variables, layers=[20],
trainers=[{'optimize': 'nag', 'learning_rate': 0.1}])
tn.fit(train_data, train_labels) | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Predicting probabilities, measuring the quality | # predict probabilities for each class
prob = tn.predict_proba(test_data)
print prob
print 'ROC AUC', roc_auc_score(test_labels, prob[:, 1]) | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Theanets multistage training
In some cases we need to continue training: i.e., we have new data or current trainer is not efficient anymore.
For this purpose there is partial_fit method, where you can continue training using different trainer or different data. | tn = TheanetsClassifier(features=variables, layers=[10, 10],
trainers=[{'optimize': 'rprop'}])
tn.fit(train_data, train_labels)
print('training complete') | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Second stage of fitting | tn.partial_fit(train_data, train_labels, **{'optimize': 'adadelta'})
# predict probabilities for each class
prob = tn.predict_proba(test_data)
print prob
print 'ROC AUC', roc_auc_score(test_labels, prob[:, 1]) | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Let's train network using Rprop algorithm | import neurolab
nl = NeurolabClassifier(features=variables, layers=[10], epochs=40, trainf=neurolab.train.train_rprop)
nl.fit(train_data, train_labels)
print('training complete') | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Pybrain | from rep.estimators import PyBrainClassifier
print PyBrainClassifier.__doc__
pb = PyBrainClassifier(features=variables, layers=[10, 2], hiddenclass=['TanhLayer', 'SigmoidLayer'])
pb.fit(train_data, train_labels)
print('training complete') | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Advantages of common interface
Let's build an ensemble of neural networks. This will be done by bagging meta-algorithm
Bagging over Theanets classifier (same can be done with any neural network)
in practice, one will need many networks to get predictions better, then obtained by one network | from sklearn.ensemble import BaggingClassifier
base_tn = TheanetsClassifier(layers=[20], trainers=[{'min_improvement': 0.01}])
bagging_tn = BaggingClassifier(base_estimator=base_tn, n_estimators=3)
bagging_tn.fit(train_data[variables], train_labels)
print('training complete')
prob = bagging_tn.predict_proba(test_data... | howto/06-howto-neural-nets.ipynb | scr4t/rep | apache-2.0 |
Gaussian Processes
model for functions/continuous output
for new input returns predicted output and uncertainty | display(Image(filename="GP_uq.png", width=630)) #source: http://scikit-learn.org/0.17/modules/gaussian_process.html | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Support Vector Machines
model for classification
map data nonlinearly to higher dimensionsal space
separate points of different classes using a plane (i.e. linearly) | display(Image(filename="SVM.png", width=700)) #source: https://en.wikipedia.org/wiki/Support_vector_machine | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Feature engineering and two classification algorithms
Feature engineering in Machine Learning
feature engineering: map data to features with function $\FM:\IS\to \RKHS$
handle nonlinear relations with linear methods ($\FM$ nonlinear)
handle non-numerical data (e.g. text) | display(Image(filename="monomials_small.jpg", width=800)) #source: Berhard Schölkopf | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Working in Feature Space
want Feature Space $\RKHS$ (the codomain of $\FM$) to be vector space to get nice mathematical structure
definition of inner products induces norms and possibility to measure angles
can use linear algebra in $\RKHS$ to solve ML problems
inner products
angles
norms
distances
induces nonlinear... | figkw = {"figsize":(4,4), "dpi":150}
np.random.seed(5)
samps_per_distr = 20
data = np.vstack([stats.multivariate_normal(np.array([-2,0]), np.eye(2)*1.5).rvs(samps_per_distr),
stats.multivariate_normal(np.array([2,0]), np.eye(2)*1.5).rvs(samps_per_distr)])
distr_idx = np.r_[[0]*samps_per_distr, [1]*sam... | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Classification using inner products in Feature Space
compute mean feature space embedding $$\mu_{0} = \frac{1}{N_0} \sum_{l_i = 0} \FM(x_i) ~~~~~~~~ \mu_{1} = \frac{1}{N_1} \sum_{l_i = 1} \FM(x_i)$$
assign test point to most similar class in terms of inner product between point and mean embedding $\prodDot{\FM(x)}{\mu... | pl.figure(**figkw)
for (idx, c, marker) in [(0,'r', (0,3,0)), (1, "b", "x")]:
pl.scatter(*data[distr_idx==idx,:].T, c=c, marker=marker, alpha=0.2)
pl.arrow(0, 0, *data[distr_idx==idx,:].mean(0), head_width=0.3, width=0.05, head_length=0.3, fc=c, ec=c)
pl.title(r"Mean embeddings for $\Phi(x)=x$");
pl.figure(**f... | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Classification using density estimation
estimate density for each class by centering a gaussian, taking mixture as estimate
$$\widehat{p}0 = \frac{1}{N_0} \sum{l_i = 0} \mathcal{N}(\cdot; x_i,\Sigma) ~~~~~~~~ \widehat{p}1 = \frac{1}{N_1} \sum{l_i = 1} \mathcal{N}(\cdot; x_i,\Sigma)$$ | # Some plotting code
def apply_to_mg(func, *mg):
#apply a function to points on a meshgrid
x = np.vstack([e.flat for e in mg]).T
return np.array([func(i.reshape((1,2))) for i in x]).reshape(mg[0].shape)
def plot_with_contour(samps, data_idx, cont_func, method_name = None, delta = 0.025, pl = pl, colormesh... | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Classification using density estimation
estimate density for each class by centering a gaussian, taking mixture as estimate
$$\widehat{p}0 = \frac{1}{N_0} \sum{l_i = 0} \mathcal{N}(\cdot; x_i,\Sigma) ~~~~~~~~ \widehat{p}1 = \frac{1}{N_1} \sum{l_i = 1} \mathcal{N}(\cdot; x_i,\Sigma)$$
assign test point $x$ to class... | class KMEclassification(object):
def __init__(self, samps1, samps2, kernel):
self.de1 = ro.RKHSDensityEstimator(samps1, kernel, 0.1)
self.de2 = ro.RKHSDensityEstimator(samps2, kernel, 0.1)
def classification_score(self, test):
return (self.de1.eval_kme(test) - self.de2.eval_kme(test... | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Applications
Kernel mean embedding
mean feature with canonical feature map $\frac{1}{N} \sum_{i = 1}^N \FM(x_i) = \frac{1}{N} \sum_{i = 1}^N \PDK(x_i, \cdot)$
this the estimate of the kernel mean embedding of the distribution/density $\rho$ of $x_i$
$$\mu_\rho(\cdot) = \int \PDK(x,\cdot) \mathrm{d}\rho(x)$$
usin... | out_samps = data[distr_idx==0,:1] + 1
inp_samps = data[distr_idx==0,1:] + 1
def plot_mean_embedding(cme, inp_samps, out_samps, p1 = 0., p2 = 1., offset = 0.5):
x = np.linspace(inp_samps.min()-offset,inp_samps.max()+offset,200)
fig = pl.figure(figsize=(10, 5))
ax = [pl.subplot2grid((2, 2), (0, 1)),
... | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
Conditional mean embedding (3)
closed form estimate given samples from input and output
$$\begin{bmatrix}\PDK_Y(y_1, \cdot),& \dots &, \PDK_Y(y_N, \cdot)\end{bmatrix} \Gram_X^{-1} \begin{bmatrix}\PDK_X(x_1, \cdot)\ \vdots \ \PDK_X(x_N, \cdot)\end{bmatrix}$$
closed form estimate of output embedding for new input $x^... | HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/MpzaCCbX-z4?rel=0&showinfo=0&start=148" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>')
display(Image(filename="Pendulum_eigenfunctions.png", width=700))
display(Image(filename="KeywordClustering.png", widt... | Tutorial_on_modern_kernel_methods.ipynb | ingmarschuster/rkhs_demo | gpl-3.0 |
In this example you will learn how to make use of the periodicity of the electrodes.
As seen in TB 4 the transmission calculation takes a considerable amount of time. In this example we will redo the same calculation, but speed it up (no approximations made).
A large computational effort is made on calculating the self... | graphene = sisl.geom.graphene(orthogonal=True) | TB_05/run.ipynb | zerothi/ts-tbt-sisl-tutorial | gpl-3.0 |
Note the below lines are differing from the same lines in TB 4, i.e. we save the electrode electronic structure without extending it 25 times. | H_elec = sisl.Hamiltonian(graphene)
H_elec.construct(([0.1, 1.43], [0., -2.7]))
H_elec.write('ELEC.nc') | TB_05/run.ipynb | zerothi/ts-tbt-sisl-tutorial | gpl-3.0 |
See TB 2 for details on why we choose repeat/tile on the Hamiltonian object and not on the geometry, prior to construction. | H = H_elec.repeat(25, axis=0).tile(15, axis=1)
H = H.remove(
H.geometry.close(
H.geometry.center(what='cell'), R=10.)
)
dangling = [ia for ia in H.geometry.close(H.geometry.center(what='cell'), R=14.)
if len(H.edges(ia)) < 3]
H = H.remove(dangling)
edge = [ia for ia in H.geometry.close(H.ge... | TB_05/run.ipynb | zerothi/ts-tbt-sisl-tutorial | gpl-3.0 |
Exercises
Instead of analysing the same thing as in TB 4 you should perform the following actions to explore the available data-analysis capabilities of TBtrans. Please note the difference in run-time between example 04 and this example. Always use Bloch's theorem when applicable!
HINT please copy as much as you like f... | tbt = sisl.get_sile('siesta.TBT.nc')
# Easier manipulation of the geometry
geom = tbt.geometry
a_dev = tbt.a_dev # the indices where we have DOS
# Extract the DOS, per orbital (hence sum=False)
DOS = tbt.ADOS(0, sum=False)
# Normalize DOS for plotting (maximum size == 400)
# This array has *all* energy points and orbit... | TB_05/run.ipynb | zerothi/ts-tbt-sisl-tutorial | gpl-3.0 |
We're first going to train a multinomial logistic regression using simple gradient descent.
TensorFlow works like this:
* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description... | # With gradient descent training, even this much (10000) data is prohibitive.
# Subset the training data for faster turnaround.
train_subset = 10000 #10000
graph = tf.Graph()
with graph.as_default():
# Input data.
# Load the training, validation and test data into constants that are
# attached to the graph.
t... | google_dl_udacity/lesson3/2_fullyconnected.ipynb | jinzishuai/learn2deeplearn | gpl-3.0 |
results
lesson 1 sklearn LogisticRegression
50 training samples: LogisticRegression score: 0.608200
100 training samples: LogisticRegression score: 0.708200
1000 training samples: LogisticRegression score: 0.829200
5000 training samples: LogisticRegression score: 0.846200
tensor flow results above
50: 43.3%
100: 53.... | batch_size = 128
graph = tf.Graph()
with graph.as_default():
# Input data. For the training data, we use a placeholder that will be fed
# at run time with a training minibatch.
tf_train_dataset = tf.placeholder(tf.float32,
shape=(batch_size, image_size * image_size))
tf_tra... | google_dl_udacity/lesson3/2_fullyconnected.ipynb | jinzishuai/learn2deeplearn | gpl-3.0 |
Demons Registration
This function will align the fixed and moving images using the Demons registration method. If given a mask, the similarity metric will be evaluated using points sampled inside the mask. If given fixed and moving points the similarity metric value and the target registration errors will be displayed ... | def demons_registration(fixed_image, moving_image, fixed_points = None, moving_points = None):
registration_method = sitk.ImageRegistrationMethod()
# Create initial identity transformation.
transform_to_displacment_field_filter = sitk.TransformToDisplacementFieldFilter()
transform_to_displacment_f... | 66_Registration_Demons.ipynb | thewtex/SimpleITK-Notebooks | apache-2.0 |
Running the Demons registration on this data will <font color="red">take a long time</font> (run it before going home). If you are less interested in accuracy you can switch the optimizer from conjugate gradient to gradient, will run much faster but the results are worse. | #%%timeit -r1 -n1
# Uncomment the line above if you want to time the running of this cell.
# Select the fixed and moving images, valid entries are in [0,9]
fixed_image_index = 0
moving_image_index = 7
tx = demons_registration(fixed_image = images[fixed_image_index],
moving_image = images[mo... | 66_Registration_Demons.ipynb | thewtex/SimpleITK-Notebooks | apache-2.0 |
SimpleITK also includes a set of Demons filters which are independent of the ImageRegistrationMethod. These include:
1. DemonsRegistrationFilter
2. DiffeomorphicDemonsRegistrationFilter
3. FastSymmetricForcesDemonsRegistrationFilter
4. SymmetricForcesDemonsRegistrationFilter
As these filters are independent of the Ima... | def smooth_and_resample(image, shrink_factor, smoothing_sigma):
"""
Args:
image: The image we want to resample.
shrink_factor: A number greater than one, such that the new image's size is original_size/shrink_factor.
smoothing_sigma: Sigma for Gaussian smoothing, this is in physical (ima... | 66_Registration_Demons.ipynb | thewtex/SimpleITK-Notebooks | apache-2.0 |
Now we will use our newly minted multiscale framework to perform registration with the Demons filters. Some things you can easily try out by editing the code below:
1. Is there really a need for multiscale - just call the multiscale_demons method without the shrink_factors and smoothing_sigmas parameters.
2. Which Demo... | # Define a simple callback which allows us to monitor the Demons filter's progress.
def iteration_callback(filter):
print('\r{0}: {1:.2f}'.format(filter.GetElapsedIterations(), filter.GetMetric()), end='')
fixed_image_index = 0
moving_image_index = 7
# Select a Demons filter and configure it.
demons_filter = sit... | 66_Registration_Demons.ipynb | thewtex/SimpleITK-Notebooks | apache-2.0 |
A Slightly Bigger Word-Document Matrix
The example word-document matrix is taken from http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/11/so-many-dimensions-and-how-to-reduce.html but expanded to cover a 3rd topic related to a home or house | # create a simple word-document matrix as a pandas dataframe, the content values have been normalised
words = ['wheel', ' seat', ' engine', ' slice', ' oven', ' boil', 'door', 'kitchen', 'roof']
print(words)
documents = ['doc1', 'doc2', 'doc3', 'doc4', 'doc5', 'doc6', 'doc7', 'doc8', 'doc9']
word_doc = pandas.DataFrame... | A03_svd_applied_to_slightly_bigger_word_document_matrix.ipynb | makeyourowntextminingtoolkit/makeyourowntextminingtoolkit | gpl-2.0 |
Yes, that worked .. the reconstructed A2 is the same as the original A (within the bounds of small floating point accuracy)
Now Reduce Dimensions, Extract Topics
Here we use only the top 3 values of the S singular value matrix, pretty brutal reduction in dimensions!
Why 3, and not 2?
We'll only plot 2 dimensions for th... | # S_reduced is the same as S but with only the top 3 elements kept
S_reduced = numpy.zeros_like(S)
# only keep top two eigenvalues
l = 3
S_reduced[:l, :l] = S[:l,:l]
# show S_rediced which has less info than original S
print("S_reduced =\n", numpy.round(S_reduced, decimals=2)) | A03_svd_applied_to_slightly_bigger_word_document_matrix.ipynb | makeyourowntextminingtoolkit/makeyourowntextminingtoolkit | gpl-2.0 |
The above shows that there are indeed 3 clusters of documents. That matches our expectations as we constructed the example data set that way.
Topics from New View of Words | # topics are a linear combination of original words
U_S_reduced = numpy.dot(U, S_reduced)
df = pandas.DataFrame(numpy.round(U_S_reduced, decimals=2), index=words)
# show colour coded so it is easier to see significant word contributions to a topic
df.style.background_gradient(cmap=plt.get_cmap('Blues'), low=0, high=2) | A03_svd_applied_to_slightly_bigger_word_document_matrix.ipynb | makeyourowntextminingtoolkit/makeyourowntextminingtoolkit | gpl-2.0 |
Operations on Tensors
Variables and Constants
Tensors in TensorFlow are either contant (tf.constant) or variables (tf.Variable).
Constant values can not be changed, while variables values can be.
The main difference is that instances of tf.Variable have methods allowing us to change
their values while tensors construc... | x = tf.constant([2, 3, 4])
x
x = tf.Variable(2.0, dtype=tf.float32, name='my_variable')
x.assign(45.8) # TODO 1
x
x.assign_add(4) # TODO 2
x
x.assign_sub(3) # TODO 3
x | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/1_core_tensorflow.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Point-wise operations
Tensorflow offers similar point-wise tensor operations as numpy does:
tf.add allows to add the components of a tensor
tf.multiply allows us to multiply the components of a tensor
tf.subtract allow us to substract the components of a tensor
tf.math.* contains the usual math operations to be appli... | a = tf.constant([5, 3, 8]) # TODO 1
b = tf.constant([3, -1, 2])
c = tf.add(a, b)
d = a + b
print("c:", c)
print("d:", d)
a = tf.constant([5, 3, 8]) # TODO 2
b = tf.constant([3, -1, 2])
c = tf.multiply(a, b)
d = a * b
print("c:", c)
print("d:", d)
# tf.math.exp expects floats so we need to explicitly give the type
a... | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/1_core_tensorflow.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
NumPy Interoperability
In addition to native TF tensors, tensorflow operations can take native python types and NumPy arrays as operands. | # native python list
a_py = [1, 2]
b_py = [3, 4]
tf.add(a_py, b_py) # TODO 1
# numpy arrays
a_np = np.array([1, 2])
b_np = np.array([3, 4])
tf.add(a_np, b_np) # TODO 2
# native TF tensor
a_tf = tf.constant([1, 2])
b_tf = tf.constant([3, 4])
tf.add(a_tf, b_tf) # TODO 3 | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/1_core_tensorflow.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Gradient Function
To use gradient descent we need to take the partial derivatives of the loss function with respect to each of the weights. We could manually compute the derivatives, but with Tensorflow's automatic differentiation capabilities we don't have to!
During gradient descent we think of the loss as a function... | # TODO 1
def compute_gradients(X, Y, w0, w1):
with tf.GradientTape() as tape:
loss = loss_mse(X, Y, w0, w1)
return tape.gradient(loss, [w0, w1])
w0 = tf.Variable(0.0)
w1 = tf.Variable(0.0)
dw0, dw1 = compute_gradients(X, Y, w0, w1)
print("dw0:", dw0.numpy())
print("dw1", dw1.numpy()) | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/1_core_tensorflow.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Quick numbers: # RRT events & total # encounters (for the main hospital)
For all patient & location types | query_TotalEncs = """
SELECT count(1)
FROM (
SELECT DISTINCT encntr_id
FROM encounter
WHERE encntr_complete_dt_tm < 4000000000000
AND loc_facility_cd = '633867'
) t;
"""
cur.execute(query_TotalEncs)
cur.fetchall() | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
For admit_type_cd!='0' & encntr_type_class_cd='391 | query_TotalEncs = """
SELECT count(1)
FROM (
SELECT DISTINCT encntr_id
FROM encounter
WHERE encntr_complete_dt_tm < 4e12
AND loc_facility_cd = '633867'
AND admit_type_cd!='0'
AND encntr_type_class_cd='391'
) t;
"""
cur.execute(query_TotalEncs)
cur.fetchall() | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Examining distribution of encounter durations (with loc_facility_cd)
Analyze the durations of the RRT event patients. | query_count = """
SELECT count(*)
FROM (
SELECT DISTINCT ce.encntr_id
FROM clinical_event ce
INNER JOIN encounter enc ON enc.encntr_id = ce.encntr_id
WHERE ce.event_cd = '54411998'
AND ce.result_status_cd NOT IN ('31', '36')
AND ce.valid_until_dt_tm > 4e12
AND ce.event_class_cd not in ('654... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Let's look at durations for inpatients WITH RRTs from the Main Hospital where encounter_admit_type is not zero | query = """
SELECT
DISTINCT ce.encntr_id
, COALESCE(tci.checkin_dt_tm, enc.arrive_dt_tm) AS checkin_dt_tm
, enc.depart_dt_tm as depart_dt_tm
, (enc.depart_dt_tm - COALESCE(tci.checkin_dt_tm, enc.arrive_dt_tm))/3600000 AS diff_hours
, enc.reason_for_visit
, enc.admit_src_cd
, enc.admit_ty... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Let's look at durations for inpatients WITHOUT RRTs from the Main Hospital where encounter_admit_type is not zero | query = """
SELECT DISTINCT
ce.encntr_id
, COALESCE(tci.checkin_dt_tm
, enc.arrive_dt_tm) AS checkin_dt_tm
, enc.depart_dt_tm as depart_dt_tm
, (enc.depart_dt_tm - COALESCE(tci.checkin_dt_tm, enc.arrive_dt_tm))/3600000 AS diff_hours
, enc.reason_for_visit
, enc.admit_src_cd
, enc.admi... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Plot both together to see how encounter duration distributions are different | plt.figure(figsize = (10,8))
df_rrt.diff_hours.plot.hist(alpha=0.4, bins=400,normed=True)
df_nonrrt.diff_hours.plot.hist(alpha=0.4, bins=800,normed=True)
plt.xlabel('Hospital Stay Durations, hours', fontsize=14)
plt.ylabel('Normalized Frequency', fontsize=14)
plt.legend(['RRT', 'Non RRT'])
plt.tick_params(labelsize=14)... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Even accounting for the hospital, inpatients status, and accounting for some admit_type_cd, the durations are still quite different betwen RRT & non-RRT.
Trying some subset vizualizations -- these show no difference | print df_nonrrt.admit_type_cd.value_counts()
print
print df_rrt.admit_type_cd.value_counts()
print df_nonrrt.admit_src_cd.value_counts()
print
print df_rrt.admit_src_cd.value_counts()
plt.figure(figsize = (10,8))
df_rrt[df_rrt.admit_type_cd=='309203'].diff_hours.plot.hist(alpha=0.4, bins=300,normed=True)
df_nonrrt[df... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Despite controlling for patient parameters, patients with RRT events stay in the hospital longer than non-RRT event having patients.
Rerun previous EDA on hospital & patient types
Let's take a step back and look at the encounter table, for all hospitals and patient types [but using corrected time duration]. | # For encounters with RRT events
query = """
SELECT DISTINCT
ce.encntr_id
, COALESCE(tci.checkin_dt_tm
, enc.arrive_dt_tm) AS checkin_dt_tm
, enc.depart_dt_tm as depart_dt_tm
, (enc.depart_dt_tm - COALESCE(tci.checkin_dt_tm, enc.arrive_dt_tm))/3600000 AS diff_hours
, enc.reason_for_visit
... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
The notebook Probe_encounter_types_classes explores admit type, class types & counts | plt.figure()
df['diff_hours'].plot.hist(bins=500)
plt.xlabel("Hospital Stay Duration, days")
plt.title("Range of stays, patients with RRT")
plt.xlim(0, 2000) | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Group by facility
We want to pull from similar patient populations | df.head()
df.loc_desc.value_counts()
grouped = df.groupby('loc_desc')
grouped.describe() | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Most number of results from 633867, or The Main Hospital | df.diff_hours.hist(by=df.loc_desc, bins=300)
# Use locations 4382264, 4382273, 633867
plt.figure(figsize=(12, 6))
df[df['loc_facility_cd']=='633867']['diff_hours'].plot.hist(alpha=0.4, bins=300,normed=True)
df[df['loc_facility_cd']=='4382264']['diff_hours'].plot.hist(alpha=0.4, bins=300,normed=True)
df[df['loc_facili... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Looks like these three locations (633867, 4382264, 4382273) have about the same distribution.
Appropriate test to verify this: 2-sample Kolmogorov-Smirnov, if you're willing to compare pairwise...other tests? Wikipedia has a good article with references: https://en.wikipedia.org/wiki/Kolmogorov–Smirnov_test. Null hypot... | from scipy.stats import ks_2samp
ks_2samp(df[df['loc_facility_cd']=='633867']['diff_hours'],df[df['loc_facility_cd']=='4382264']['diff_hours'])
# Critical test statistic at alpha = 0.05: = 1.36 * sqrt((n1+n2)/n1*n2) = 1.36*(sqrt((1775+582)/(1775*582)) = 0.065
# 0.074 > 0.065 -> null hypothesis rejected at level 0.05.... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
From scipy documentation: "If the KS statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions of the two samples are the same"
Null hypothesis: the distributions are the same.
Looks like samples from 4382273 are different... plot that & 633867 | plt.figure(figsize=(10,8))
df[df['loc_facility_cd']=='633867']['diff_hours'].plot.hist(alpha=0.4, bins=500,normed=True)
df[df['loc_facility_cd']=='4382273']['diff_hours'].plot.hist(alpha=0.4, bins=700,normed=True)
plt.xlabel('Hospital Stay Durations, hours')
plt.legend(['633867', '4382273'])
plt.xlim(0, 1000) | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Let's compare encounter duration histograms for patients with RRT & without RRT events, and see if there is a right subset of data to be selected for modeling
(There is) | df.columns
df.admit_src_desc.value_counts()
df.enc_type_class_desc.value_counts()
# vast majority are inpatient
df.enc_type_desc.value_counts()
df.admit_type_desc.value_counts() | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Plot RRT & non-RRT with different codes | # For encounters without RRT events, from Main Hospital.
# takes a while to run -- several minutes
query = """
SELECT DISTINCT
ce.encntr_id
, COALESCE(tci.checkin_dt_tm
, enc.arrive_dt_tm) AS checkin_dt_tm
, enc.depart_dt_tm as depart_dt_tm
, (enc.depart_dt_tm - COALESCE(tci.checkin_dt_tm, enc.ar... | Data Science Notebooks/Notebooks/EDA/encounter_durations[EDA].ipynb | nikitaswinnen/model-for-predicting-rapid-response-team-events | apache-2.0 |
Softmax Classifier
Sanity Check: Overfit Small Portion | script = """
source("breastcancer/softmax_clf.dml") as clf
# Hyperparameters & Settings
lr = 1e-2 # learning rate
mu = 0.9 # momentum
decay = 0.999 # learning rate decay constant
batch_size = 32
epochs = 500
log_interval = 1
n = 200 # sample size for overfitting sanity check
# Train
[W, b] = clf::train(X[1:n,], Y... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 |
Train | script = """
source("breastcancer/softmax_clf.dml") as clf
# Hyperparameters & Settings
lr = 5e-7 # learning rate
mu = 0.5 # momentum
decay = 0.999 # learning rate decay constant
batch_size = 32
epochs = 1
log_interval = 10
# Train
[W, b] = clf::train(X, Y, X_val, Y_val, lr, mu, decay, batch_size, epochs, log_inte... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 |
Eval | script = """
source("breastcancer/softmax_clf.dml") as clf
# Eval
probs = clf::predict(X, W, b)
[loss, accuracy] = clf::eval(probs, Y)
probs_val = clf::predict(X_val, W, b)
[loss_val, accuracy_val] = clf::eval(probs_val, Y_val)
"""
outputs = ("loss", "accuracy", "loss_val", "accuracy_val")
script = dml(script).input(X... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 |
LeNet-like ConvNet
Sanity Check: Overfit Small Portion | script = """
source("breastcancer/convnet.dml") as clf
# Hyperparameters & Settings
lr = 1e-2 # learning rate
mu = 0.9 # momentum
decay = 0.999 # learning rate decay constant
lambda = 0 #5e-04
batch_size = 32
epochs = 300
log_interval = 1
dir = "models/lenet-cnn/sanity/"
n = 200 # sample size for overfitting sani... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 |
Hyperparameter Search | script = """
source("breastcancer/convnet.dml") as clf
dir = "models/lenet-cnn/hyperparam-search/"
# TODO: Fix `parfor` so that it can be efficiently used for hyperparameter tuning
j = 1
while(j < 2) {
#parfor(j in 1:10000, par=6) {
# Hyperparameter Sampling & Settings
lr = 10 ^ as.scalar(rand(rows=1, cols=1, min... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 |
Train | ml.setStatistics(True)
ml.setExplain(True)
# sc.setLogLevel("OFF")
script = """
source("breastcancer/convnet_distrib_sgd.dml") as clf
# Hyperparameters & Settings
lr = 0.00205 # learning rate
mu = 0.632 # momentum
decay = 0.99 # learning rate decay constant
lambda = 0.00385
batch_size = 1
parallel_batches = 19
ep... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 |
Eval | script = """
source("breastcancer/convnet_distrib_sgd.dml") as clf
# Eval
probs = clf::predict(X, C, Hin, Win, Wc1, bc1, Wc2, bc2, Wc3, bc3, Wa1, ba1, Wa2, ba2)
[loss, accuracy] = clf::eval(probs, Y)
probs_val = clf::predict(X_val, C, Hin, Win, Wc1, bc1, Wc2, bc2, Wc3, bc3, Wa1, ba1, Wa2, ba2)
[loss_val, accuracy_val]... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 |
# script = """
# N = 102400 # num examples
# C = 3 # num input channels
# Hin = 256 # input height
# Win = 256 # input width
# X = rand(rows=N, cols=C*Hin*Win, pdf="normal")
# """
# outputs = "X"
# script = dml(script).output(*outputs)
# thisX = ml.execute(script).get(*outputs)
# thisX
# script = """
# f = functio... | projects/breast_cancer/MachineLearning.ipynb | dusenberrymw/incubator-systemml | apache-2.0 | |
Create and fit Spark ML model | from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
# Create feature vectors. Ignore arr_delay and it's derivate, is_late
feature_assembler = VectorAssembler(
inputCols=[x for x in training.columns if x not in ["is_late","arrdelay"... | spark/Logistic Regression Example.ipynb | zoltanctoth/bigdata-training | gpl-2.0 |
Predict whether the aircraft will be late | predicted = model.transform(test)
predicted.take(1) | spark/Logistic Regression Example.ipynb | zoltanctoth/bigdata-training | gpl-2.0 |
Check model performance | predicted = predicted.withColumn("is_late",is_late(predicted.arrdelay))
predicted.crosstab("is_late","prediction").show() | spark/Logistic Regression Example.ipynb | zoltanctoth/bigdata-training | gpl-2.0 |
The data goes all the way back to 1967 and is updated weekly.
Blaze provides us with the first 10 rows of the data for display. Just to confirm, let's just count the number of rows in the Blaze expression: | fred_ccsa.count() | notebooks/data/quandl.fred_ccsa/notebook.ipynb | quantopian/research_public | apache-2.0 |
Let's go plot it for fun. This data set is definitely small enough to just put right into a Pandas DataFrame | unrate_df = odo(fred_ccsa, pd.DataFrame)
unrate_df.plot(x='asof_date', y='value')
plt.xlabel("As Of Date (asof_date)")
plt.ylabel("Unemployment Claims")
plt.title("United States Unemployment Claims")
plt.legend().set_visible(False)
unrate_recent = odo(fred_ccsa[fred_ccsa.asof_date >= '2002-01-01'], pd.DataFrame)
unr... | notebooks/data/quandl.fred_ccsa/notebook.ipynb | quantopian/research_public | apache-2.0 |
Table of Contents
Outer Join Operator
CHAR datatype size increase
Binary Data Type
Boolean Data Type
Synonyms for Data Types
Function Synonymns
Netezza Compatibility
Select Enhancements
Hexadecimal Functions
Table Creation with Data
<a id='outer'></a>
Outer Join Operator
Db2 allows the use of the ... | %%sql
SELECT DEPTNAME, LASTNAME FROM
DEPARTMENT D LEFT OUTER JOIN EMPLOYEE E
ON D.DEPTNO = E.WORKDEPT | v1/Db2 11 Compatibility Features.ipynb | DB2-Samples/db2jupyter | apache-2.0 |
TRANSLATE Function
The translate function syntax in Db2 is:
<pre>
TRANSLATE(expression, to_string, from_string, padding)
</pre>
The TRANSLATE function returns a value in which one or more characters in a string expression might
have been converted to other characters. The function converts all the characters in char-... | %%sql
SET SQL_COMPAT = 'NPS';
VALUES TRANSLATE('Hello'); | v1/Db2 11 Compatibility Features.ipynb | DB2-Samples/db2jupyter | apache-2.0 |
OFFSET Extension
The FETCH FIRST n ROWS ONLY clause can also include an OFFSET keyword. The OFFSET keyword
allows you to retrieve the answer set after skipping "n" number of rows. The syntax of the OFFSET
keyword is:
<pre>
OFFSET n ROWS FETCH FIRST x ROWS ONLY
</pre>
The OFFSET n ROWS must precede the FETCH FIRST x R... | %%sql
SELECT LASTNAME FROM EMPLOYEE
FETCH FIRST 10 ROWS ONLY | v1/Db2 11 Compatibility Features.ipynb | DB2-Samples/db2jupyter | apache-2.0 |
Back to Top
<a id="create"><a/>
Table Creation Extensions
The CREATE TABLE statement can now use a SELECT clause to generate the definition and LOAD the data
at the same time.
Create Table Syntax
The syntax of the CREATE table statement has been extended with the AS (SELECT ...) WITH DATA clause:
<pre>
CREATE TABLE <n... | %sql -q DROP TABLE AS_EMP
%sql CREATE TABLE AS_EMP AS (SELECT EMPNO, SALARY+BONUS FROM EMPLOYEE) DEFINITION ONLY; | v1/Db2 11 Compatibility Features.ipynb | DB2-Samples/db2jupyter | apache-2.0 |
A growing collection of tasks is readily available in pyannote.audio.tasks... | from pyannote.audio.tasks import __all__ as TASKS; print('\n'.join(TASKS)) | tutorials/add_your_own_task.ipynb | pyannote/pyannote-audio | mit |
... but you will eventually want to use pyannote.audio to address a different task.
In this example, we will add a new task addressing the sound event detection problem.
Problem specification
A problem is expected to be solved by a model $f$ that takes an audio chunk $X$ as input and returns its predicted solution $\h... | from pyannote.audio.core.task import Resolution
resolution = Resolution.CHUNK | tutorials/add_your_own_task.ipynb | pyannote/pyannote-audio | mit |
Type of problem
Similarly, the type of your problem may fall into one of these generic machine learning categories:
* Problem.BINARY_CLASSIFICATION for binary classification
* Problem.MONO_LABEL_CLASSIFICATION for multi-class classification
* Problem.MULTI_LABEL_CLASSIFICATION for multi-label classification
* Problem.... | from pyannote.audio.core.task import Problem
problem = Problem.MULTI_LABEL_CLASSIFICATION
from pyannote.audio.core.task import Specifications
specifications = Specifications(
problem=problem,
resolution=resolution,
duration=5.0,
classes=["Speech", "Dog", "Cat", "Alarm_bell_ringing", "Dishes",
... | tutorials/add_your_own_task.ipynb | pyannote/pyannote-audio | mit |
A task is expected to be solved by a model $f$ that (usually) takes an audio chunk $X$ as input and returns its predicted solution $\hat{y} = f(X)$.
To help training the model $f$, the task $\mathcal{T}$ is in charge of
- generating $(X, y)$ training samples using the dataset
- defining the loss function $\mathcal{L... | from typing import Optional
import torch
import torch.nn as nn
import numpy as np
from pyannote.core import Annotation
from pyannote.audio import Model
from pyannote.audio.core.task import Task, Resolution
# Your custom task must be a subclass of `pyannote.audio.core.task.Task`
class SoundEventDetection(Task):
"""... | tutorials/add_your_own_task.ipynb | pyannote/pyannote-audio | mit |
Вы могли заметить, что мы нигде не объявили тип переменных a, b и c. В Python этого делать не надо. Язык сам выберет тип по значению, которое вы положили в переменную. Для переменной a это тип int (целое число). Для b — str (строка). Для c — float (вещественное число).
В ближайшем будущем вы скорее всего позн... | a = 5.0
s = "LKSH students are awesome =^_^="
print(type(a))
print(type(b)) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Параллельное присваивание
В Python можно присвоит значения сразу нескольким переменным: | a, b = 3, 5
print(a)
print(b) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
При этом Python сначала вычисляет все значения справа, а потом уже присваивает вычисленные значения переменным слева: | a = 3
b = 5
a, b = b, a + b
print(a)
print(b) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Это позволяет, например, поменять значения двух переменных в одну строку: | a = "apple"
b = "banana"
a, b = b, a
print(a)
print(b) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Ввод-вывод
Как вы уже видели, для вывода на экран в Python есть функция print. Ей можно передавать несколько значений через запятую — они будут выведены в одной строке через пробел: | a = 2
b = 3
print(a, "+", b, "=", a + b) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Для ввода с клавиатуры есть функция input. Она считывает одну строку целиком: | a = input()
b = input()
print(a + b) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Ага, что-то пошло не так! Мы получили 23 вместо 5. Так произошло, потому что input() возращает строку (str), а не число (int). Чтобы это исправить нам надо явно преобразовать результат функции input() к типу int. | a = int(input())
b = int(input())
print(a + b) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Так-то лучше :)
Частая ошибка — забыть внутренние скобки после функции input. Давайте посмотрим, что в этом случае произойдёт: | a = int(input)
b = int(input)
print(a + b) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Эту ошибку можно перевести с английского так:
ОшибкаТипа: аргумент функции int() должен быть строкой, последовательностью байтов или числом, а не функцией
Теперь вы знаете что делать, если получите такую ошибку ;)
Арифметические операции
Давайте научимся складывать, умножать, вычитать и производить другие операции с це... | print(11 + 7, 11 - 7, 11 * 7, (2 + 9) * (12 - 5)) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Вещественное деление всегда даёт вещественное число (float) в результате, независимо от аргументов (если делитель не 0): | print(12 / 8, 12 / 4, 12 / -7) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Результат целочисленного деления — это результат вещественного деления, округлённый до ближайшего меньшего целого: | print(12 // 8, 12 // 4, 12 // -7) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Остаток от деления — это то что осталось от числа после целочисленного деления.
Если c = a // b, то a можно представить в виде a = c * b + r. В этом случае r — это остаток от деления.
Пример: a = 20, b = 8, c = a // b = 2. Тогда a = c * b + r превратится в 20 = 2 * 8 + 4. Остаток от деления — 4. | print(12 % 8, 12 % 4, 12 % -7) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Возведение a в степень b — это перемножение a на само себя b раз. В математике обозначается как $a^b$. | print(5 ** 2, 2 ** 4, 13 ** 0) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Возведение в степень работает для вещественных a и отрицательных b. Число в отрицательной степени — это единица делённое на то же число в положительной степени: $a^{-b} = \frac{1}{a^b}$ | print(2.5 ** 2, 2 ** -3) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Давайте посмотрим что получится, если возвести в большую степень целое число: | print(5 ** 100) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
В отличии от C++ или Pascal, Python правильно считает результат, даже если в результате получается очень большое число.
А что если возвести вещественное число в большую степень? | print(5.0 ** 100) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Запись вида <число>e<степень> — это другой способ записать $\text{<число>} \cdot 10^\text{<степень>}$. То есть:
$$\text{7.888609052210118e+69} = 7.888609052210118 \cdot 10^{69}$$
а это то же самое, что и 7888609052210118000000000000000000000000000000000000000000000000000000.
Этот результат не настолько точе... | print(2 ** 0.5, 9 ** 0.5) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
В школе вам, наверное, рассказывали, что квадратный корень нельзя извлекать из отрицательных чисел. С++ и Pascal при попытке сделать это выдадут ошибку. Давайте посмотрим, что сделает Python: | print((-4) ** 0.5) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
В общем, это не совсем правда. Извлекать квадратный корень из отрицательных чисел, всё-таки, можно, но в результате получится не вещественное, а так называемое комплексное число. Если вы получили страшную такую штуку в своей программе, скорее всего ваш код взял корень из отрицательного числа, а значит вам надо искать в... | a = 4
b = 11
c = (a ** 2 + b * 3) / (9 - b % (a + 1))
print(c) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
В примере выше переменной c присвоено значение выражения
$$\frac{a^2 + b \cdot 3}{9 - b \text{ mod } (a + 1)}$$
При отсутствии скобок арфиметические операции в выражении вычисляются в порядке приоритета (см. таблицу выше). Сначала выполняются операции с приоритетом 1, потом с приоритетом 2 и т.д. При одинаковом приорит... | print(2 * 2 + 2)
print(2 * (2 + 2)) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Преобразование типов
Если у вас есть значение одного типа, то вы можете преобразовать его к другому типу, вызвав функцию с таким же именем: | a = "-15"
print(a, int(a), float(a)) | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
Больше примеров: | # a_int, b_float, c_str - это просто имена переменных.
# Они так названы, чтобы было проще разобраться, где какое значение лежит.
a_int = 3
b_float = 5.0
c_str = "10"
print(a_int, b_float, c_str)
# При попытке сложить без преобразования мы получили бы ошибку, потому что Python
# не умеет складывать числа со строками.
... | crash-course/variables-and-expressions.ipynb | citxx/sis-python | mit |
梯度提升树（Gradient Boosted Trees）：模型理解
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https://tensorflow.google.cn/tutorials/estimator/boosted_trees_model_understanding"><img src="https://tensorflow.google.cn/images/tf_logo_32px.png"> 在 TensorFlow.org 上查看</a> </td>
<td> <a tar... | !pip install statsmodels
import numpy as np
import pandas as pd
from IPython.display import clear_output
# Load dataset.
dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')
y_train = dftrain.pop('surv... | site/zh-cn/tutorials/estimator/boosted_trees_model_understanding.ipynb | tensorflow/docs-l10n | apache-2.0 |
有关特征的描述，请参阅之前的教程。
创建特征列, 输入函数并训练 estimator
数据预处理
特征处理，使用原始的数值特征和独热编码（one-hot-encoding）处理过的非数值特征（如性别，舱位）别建立数据集。 | fc = tf.feature_column
CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',
'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']
def one_hot_cat_column(feature_name, vocab):
return fc.indicator_column(
fc.categorical_column_with_vocabulary_list(feature_name,... | site/zh-cn/tutorials/estimator/boosted_trees_model_understanding.ipynb | tensorflow/docs-l10n | apache-2.0 |
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