markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
values |
|---|---|---|---|---|
Test the converted models
To prove these models are still accurate after conversion and quantization, we'll use both of them to make predictions and compare these against our test results: | # Instantiate an interpreter for each model
sine_model = tf.lite.Interpreter('sine_model.tflite')
sine_model_quantized = tf.lite.Interpreter('sine_model_quantized.tflite')
# Allocate memory for each model
sine_model.allocate_tensors()
sine_model_quantized.allocate_tensors()
# Get the input and output tensors so we ca... | tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb | gunan/tensorflow | apache-2.0 |
We can see from the graph that the predictions for the original model, the converted model, and the quantized model are all close enough to be indistinguishable. This means that our quantized model is ready to use!
We can print the difference in file size: | import os
basic_model_size = os.path.getsize("sine_model.tflite")
print("Basic model is %d bytes" % basic_model_size)
quantized_model_size = os.path.getsize("sine_model_quantized.tflite")
print("Quantized model is %d bytes" % quantized_model_size)
difference = basic_model_size - quantized_model_size
print("Difference i... | tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb | gunan/tensorflow | apache-2.0 |
Our quantized model is only 16 bytes smaller than the original version, which only a tiny reduction in size! At around 2.6 kilobytes, this model is already so small that the weights make up only a small fraction of the overall size, meaning quantization has little effect.
More complex models have many more weights, mea... | # Install xxd if it is not available
!apt-get -qq install xxd
# Save the file as a C source file
!xxd -i sine_model_quantized.tflite > sine_model_quantized.cc
# Print the source file
!cat sine_model_quantized.cc | tensorflow/lite/micro/examples/hello_world/create_sine_model.ipynb | gunan/tensorflow | apache-2.0 |
Reshaping DataFrame objects
In the context of a single DataFrame, we are often interested in re-arranging the layout of our data.
This dataset in from Table 6.9 of Statistical Methods for the Analysis of Repeated Measurements by Charles S. Davis, pp. 161-163 (Springer, 2002). These data are from a multicenter, randomi... | cdystonia = pd.read_csv("../data/cdystonia.csv", index_col=None)
cdystonia.head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Have a peek at the structure of the index of the stacked data (and the data itself).
To complement this, unstack pivots from rows back to columns. | stacked.unstack().head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Exercise
Which columns uniquely define a row? Create a DataFrame called cdystonia2 with a hierarchical index based on these columns. | # Write your answer here | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
If we want to transform this data so that repeated measurements are in columns, we can unstack the twstrs measurements according to obs. | twstrs_wide = cdystonia2['twstrs'].unstack('obs')
twstrs_wide.head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
We can now merge these reshaped outcomes data with the other variables to create a wide format DataFrame that consists of one row for each patient. | cdystonia_wide = (cdystonia[['patient','site','id','treat','age','sex']]
.drop_duplicates()
.merge(twstrs_wide, right_index=True, left_on='patient', how='inner'))
cdystonia_wide.head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
This illustrates the two formats for longitudinal data: long and wide formats. Its typically better to store data in long format because additional data can be included as additional rows in the database, while wide format requires that the entire database schema be altered by adding columns to every row as data are co... | (cdystonia[['patient','site','id','treat','age','sex']]
.drop_duplicates()
.merge(twstrs_wide, right_index=True, left_on='patient', how='inner')
.head()) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
This approach of seqentially calling methods is called method chaining, and despite the fact that it creates very long lines of code that must be properly justified, it allows for the writing of rather concise and readable code. Method chaining is possible because of the pandas convention of returning copies of the res... | cdystonia_subset = cdystonia[['patient','site','id','treat','age','sex']]
cdystonia_complete = cdystonia_subset.drop_duplicates()
cdystonia_merged = cdystonia_complete.merge(twstrs_wide, right_index=True, left_on='patient', how='inner')
cdystonia_merged.head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
This necessitates the creation of a slew of intermediate variables that we really don't need.
Let's transform another dataset using method chaining. The measles.csv file contains de-identified cases of measles from an outbreak in Sao Paulo, Brazil in 1997. The file contains rows of individual records: | measles = pd.read_csv("../data/measles.csv", index_col=0, encoding='latin-1', parse_dates=['ONSET'])
measles.head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
The goal is to summarize this data by age groups and bi-weekly period, so that we can see how the outbreak affected different ages over the course of the outbreak.
The best approach is to build up the chain incrementally. We can begin by generating the age groups (using cut) and grouping by age group and the date (ONSE... | (measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))
.groupby(['ONSET', 'AGE_GROUP'])) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
What we then want is the number of occurences in each combination, which we can obtain by checking the size of each grouping: | (measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))
.groupby(['ONSET', 'AGE_GROUP'])
.size()).head(10) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
This results in a hierarchically-indexed Series, which we can pivot into a DataFrame by simply unstacking: | (measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))
.groupby(['ONSET', 'AGE_GROUP'])
.size()
.unstack()).head(5) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Now, fill replace the missing values with zeros: | (measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))
.groupby(['ONSET', 'AGE_GROUP'])
.size()
.unstack()
.fillna(0)).head(5) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Finally, we want the counts in 2-week intervals, rather than as irregularly-reported days, which yields our the table of interest: | case_counts_2w = (measles.assign(AGE_GROUP=pd.cut(measles.YEAR_AGE, [0,5,10,15,20,25,30,35,40,100], right=False))
.groupby(['ONSET', 'AGE_GROUP'])
.size()
.unstack()
.fillna(0)
.resample('2W')
... | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
From this, it is easy to create meaningful plots and conduct analyses: | case_counts_2w.plot(cmap='hot') | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Pivoting
The pivot method allows a DataFrame to be transformed easily between long and wide formats in the same way as a pivot table is created in a spreadsheet. It takes three arguments: index, columns and values, corresponding to the DataFrame index (the row headers), columns and cell values, respectively.
For exampl... | cdystonia.pivot(index='patient', columns='obs', values='twstrs').head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Exercise
Try pivoting the cdystonia DataFrame without specifying a variable for the cell values: | # Write your answer here | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Data transformation
There are a slew of additional operations for DataFrames that we would collectively refer to as transformations which include tasks such as:
removing duplicate values
replacing values
grouping values.
Dealing with duplicates
We can easily identify and remove duplicate values from DataFrame objects... | vessels = pd.read_csv('../data/AIS/vessel_information.csv')
vessels.tail(10)
vessels.duplicated(subset='names').tail(10) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
These rows can be removed using drop_duplicates | vessels.drop_duplicates(['names']).tail(10) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Alternately, if we simply want to replace particular values in a Series or DataFrame, we can use the replace method.
An example where replacement is useful is replacing sentinel values with an appropriate numeric value prior to analysis. A large negative number is sometimes used in otherwise positive-valued data to de... | scores = pd.Series([99, 76, 85, -999, 84, 95]) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
In such situations, we can use replace to substitute nan where the sentinel values occur. | scores.replace(-999, np.nan) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Inidcator variables
For some statistical analyses (e.g. regression models or analyses of variance), categorical or group variables need to be converted into columns of indicators--zeros and ones--to create a so-called design matrix. The Pandas function get_dummies (indicator variables are also known as dummy variables)... | # Write your answer here | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
We can now apply get_dummies to the vessel type to create 5 indicator variables. | pd.get_dummies(vessels5.type).head(10) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Discretization
Pandas' cut function can be used to group continuous or countable data in to bins. Discretization is generally a very bad idea for statistical analysis, so use this function responsibly!
Lets say we want to bin the ages of the cervical dystonia patients into a smaller number of groups: | cdystonia.age.describe() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Alternatively, one can specify custom quantiles to act as cut points: | quantiles = pd.qcut(vessels.max_loa, [0, 0.01, 0.05, 0.95, 0.99, 1])
quantiles[:30] | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Exercise
Use the discretized segment lengths as the input for get_dummies to create 5 indicator variables for segment length: | # Write your answer here | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Categorical Variables
One of the keys to maximizing performance in pandas is to use the appropriate types for your data wherever possible. In the case of categorical data--either the ordered categories as we have just created, or unordered categories like race, gender or country--the use of the categorical to encode st... | cdystonia_cat = cdystonia.assign(treatment=cdystonia.treat.astype('category')).drop('treat', axis=1)
cdystonia_cat.dtypes
cdystonia_cat.treatment.head()
cdystonia_cat.treatment.cat.codes | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
This creates an unordered categorical variable. To create an ordinal variable, we can specify order=True as an argument to astype: | cdystonia.treat.astype('category', ordered=True).head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
However, this is not the correct order; by default, the categories will be sorted alphabetically, which here gives exactly the reverse order that we need.
To specify an arbitrary order, we can used the set_categories method, as follows: | cdystonia.treat.astype('category').cat.set_categories(['Placebo', '5000U', '10000U'], ordered=True).head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Notice that we obtained set_categories from the cat attribute of the categorical variable. This is known as the category accessor, and is a device for gaining access to Categorical variables' categories, analogous to the string accessor that we have seen previously from text variables. | cdystonia_cat.treatment.cat | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Additional categoried can be added, even if they do not currently exist in the DataFrame, but are part of the set of possible categories: | cdystonia_cat['treatment'] = (cdystonia.treat.astype('category').cat
.set_categories(['Placebo', '5000U', '10000U', '20000U'], ordered=True)) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
To complement this, we can remove categories that we do not wish to retain: | cdystonia_cat.treatment.cat.remove_categories('20000U').head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Or, even more simply: | cdystonia_cat.treatment.cat.remove_unused_categories().head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
For larger datasets, there is an appreciable gain in performance, both in terms of speed and memory usage. | vessels_merged = (pd.read_csv('../data/AIS/vessel_information.csv', index_col=0)
.merge(pd.read_csv('../data/AIS/transit_segments.csv'), left_index=True, right_on='mmsi'))
vessels_merged['registered'] = vessels_merged.flag.astype('category')
%timeit vessels_merged.groupby('flag').avg_sog.mea... | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
The add_prefix and add_suffix methods can be used to give the columns of the resulting table labels that reflect the transformation: | cdystonia_grouped.mean().add_suffix('_mean').head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Exercise
Use the quantile method to generate the median values of the twstrs variable for each patient. | # Write your answer here | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
It is easy to do column selection within groupby operations, if we are only interested split-apply-combine operations on a subset of columns: | %timeit cdystonia_grouped['twstrs'].mean().head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Or, as a DataFrame: | cdystonia_grouped[['twstrs']].mean().head() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
By default, groupby groups by row, but we can specify the axis argument to change this. For example, we can group our columns by dtype this way: | dict(list(cdystonia.groupby(cdystonia.dtypes, axis=1))) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Its also possible to group by one or more levels of a hierarchical index. Recall cdystonia2, which we created with a hierarchical index: | cdystonia2.head(10) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
The level argument specifies which level of the index to use for grouping. | cdystonia2.groupby(level='obs', axis=0)['twstrs'].mean() | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Apply
We can generalize the split-apply-combine methodology by using apply function. This allows us to invoke any function we wish on a grouped dataset and recombine them into a DataFrame.
The function below takes a DataFrame and a column name, sorts by the column, and takes the n largest values of that column. We can ... | def top(df, column, n=5):
return df.sort_index(by=column, ascending=False)[:n] | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
To see this in action, consider the vessel transit segments dataset (which we merged with the vessel information to yield segments_merged). Say we wanted to return the 3 longest segments travelled by each ship: | goo = vessels_merged.groupby('mmsi')
top3segments = vessels_merged.groupby('mmsi').apply(top, column='seg_length', n=3)[['names', 'seg_length']]
top3segments.head(15) | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Notice that additional arguments for the applied function can be passed via apply after the function name. It assumes that the DataFrame is the first argument.
Exercise
Load the dataset in titanic.xls. It contains data on all the passengers that travelled on the Titanic. | from IPython.core.display import HTML
HTML(filename='../data/titanic.html') | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Women and children first?
Use the groupby method to calculate the proportion of passengers that survived by sex.
Calculate the same proportion, but by class and sex.
Create age categories: children (under 14 years), adolescents (14-20), adult (21-64), and senior(65+), and calculate survival proportions by age category... | # Write your answer here | notebooks/1.4 - Pandas Best Practices.ipynb | fonnesbeck/ngcm_pandas_2016 | cc0-1.0 |
Help with commands
If you ever need to look up a command, you can bring up the list of shortcuts by pressing H in command mode. The keyboard shortcuts are also available above in the Help menu. Go ahead and try it now.
Creating new cells
One of the most common commands is creating new cells. You can create a cell above... | ## Practice here
def fibo(n): # Recursive Fibonacci sequence!
if n == 0:
return 0
elif n == 1:
return 1
return fibo(n-1) + fibo(n-2) | nd101 Deep Learning Nanodegree Foundation/notebooks/1 - playing with jupyter/keyboard-shortcuts.ipynb | anandha2017/udacity | mit |
Line numbers
A lot of times it is helpful to number the lines in your code for debugging purposes. You can turn on numbers by pressing L (in command mode of course) on a code cell.
Exercise: Turn line numbers on and off in the above code cell.
Deleting cells
Deleting cells is done by pressing D twice in a row so D, ... | # below this cell
# Move this cell down | nd101 Deep Learning Nanodegree Foundation/notebooks/1 - playing with jupyter/keyboard-shortcuts.ipynb | anandha2017/udacity | mit |
Next, we will import the data we saved previously using the pickle library. | pickle_file = '-basic_data.pickle'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
X = save['X']
y = save['y']
char_to_int = save['char_to_int']
int_to_char = save['int_to_char']
del save # hint to help gc free up memory
print('Training set', X.shape, y.shape) | notebooks/week-6/02-using a pre-trained model with Keras.ipynb | kkkddder/dmc | apache-2.0 |
Now we need to define the Keras model. Since we will be loading parameters from a pre-trained model, this needs to match exactly the definition from the previous lab section. The only difference is that we will comment out the dropout layer so that the model uses all the hidden neurons when doing the predictions. | # define the LSTM model
model = Sequential()
model.add(LSTM(128, return_sequences=False, input_shape=(X.shape[1], X.shape[2])))
# model.add(Dropout(0.50))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam') | notebooks/week-6/02-using a pre-trained model with Keras.ipynb | kkkddder/dmc | apache-2.0 |
Next we will load the parameters from the model we trained previously, and compile it with the same loss and optimizer function. | # load the parameters from the pretrained model
filename = "-basic_LSTM.hdf5"
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam') | notebooks/week-6/02-using a pre-trained model with Keras.ipynb | kkkddder/dmc | apache-2.0 |
We also need to rewrite the sample() and generate() helper functions so that we can use them in our code: | def sample(preds, temperature=1.0):
preds = np.asarray(preds).astype('float64')
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
def generate(sentence, sample_length=50, diver... | notebooks/week-6/02-using a pre-trained model with Keras.ipynb | kkkddder/dmc | apache-2.0 |
Now we can use the generate() function to generate text of any length based on our imported pre-trained model and a seed text of our choice. For best result, the length of the seed text should be the same as the length of training sequences (100 in the previous lab section).
In this case, we will test the overfitting ... | prediction_length = 500
seed_from_text = "america has shown that progress is possible. last year, income gains were larger for households at t"
seed_original = "and as people around the world began to hear the tale of the lowly colonists who overthrew an empire"
for seed in [seed_from_text, seed_original]:
generat... | notebooks/week-6/02-using a pre-trained model with Keras.ipynb | kkkddder/dmc | apache-2.0 |
Data input
We need some data to get started. Luckily, we have jQAssistant at our hand. It's integrated into the build process of Spring PetClinic repository above and scanned the Git repository information automatically with every executed build.
So let's query our almighty Neo4j graph database that holds all the stru... | graph = py2neo.Graph()
query = """
MATCH (author:Author)-[:COMMITED]-> (commit:Commit)
RETURN author.name as name, author.email as email
"""
result = graph.data(query)
# just how first three entries
result[0:3] | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
The query returns all commits with their authors and the author's email addresses. We get some nice, tabular data that we put into Pandas's DataFrame. | commits = pd.DataFrame(result)
commits.head() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Familiarization
First, I like to check the raw data a little bit. I often do this by first having a look at the data types the data source is returning. It's a good starting point to check that Pandas recognizes the data types accordingly. You can also use this approach to check for skewed data columns very quickly (es... | commits.dtypes | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
That's OK for our simple scenario. The two columns with texts are objects – nothing spectacular.
In the next step, I always like to get a "feeling" of all the data. Primarily, I want to get a quick impression of the data quality again. It could always be that there is "dirty data" in the dataset or that there are... | commits['name'].value_counts()[0:10] | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
OK, at first glance, something seems awkward. Let's have a look at the email addresses. | commits['email'].value_counts()[0:10] | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
OK, the bad feeling is strengthening. We might have a problem with multiple authors having multiple email addresses. Let me show you the problem by better representing the problem.
Interlude - begin
In the interlude section, I take you to a short, mostly undocumented excursion with probably messy code (don't do this at... | grouped_by_authors = commits[['name', 'email']]\
.drop_duplicates().groupby('name').count()\
.sort_values('email', ascending=False).reset_index().reset_index()
grouped_by_authors.head() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Same procedure for the email addresses. | grouped_by_email = commits[['name', 'email']]\
.drop_duplicates().groupby('email').count()\
.sort_values('name', ascending=False).reset_index().reset_index()
grouped_by_email.head() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Then I merge the two DataFrames with a subset of the original data. I get each author and email index as well as the number of occurrences for author respectively emails. I only need the ones that are occurring multiple times, so I check for > 2. | plot_data = commits.drop_duplicates()\
.merge(grouped_by_authors, left_on='name', right_on="name", suffixes=["", "_from_authors"], how="outer")\
.merge(grouped_by_email, left_on='email', right_on="email", suffixes=["", "_from_emails"], how="outer")
plot_data = plot_data[\
(plot_data['emai... | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
I just add some nicely normalized indexes for plotting (note: there might be a method that's easier) | from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(plot_data['index'])
plot_data['normalized_index_name'] = le.transform(plot_data['index']) * 10
le.fit(plot_data['index_from_emails'])
plot_data['normalized_index_email'] = le.transform(plot_data['index_from_emails']) * 10
plot_data.head() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Plot an assignment table with the relationships between authors and email addresses. | fig1 = plt.figure(facecolor='white')
ax1 = plt.axes(frameon=False)
ax1.set_frame_on(False)
ax1.get_xaxis().tick_bottom()
ax1.axes.get_yaxis().set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
# simply plot all the data (imperfection: duplicated will be displayed in bold font)
for data in plot_data.iterrows():... | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Alright! Here we are! We see that multiple authors use multiple email addresses. And I see a pattern that could be used to get better data. Do you, too?
Interlude - end
If you skipped the interlude section: I just visualized / demonstrated that there are different email addresses per author (and vise versa). Some auth... | commits['nickname'] = commits['email'].apply(lambda x : x.split("@")[0])
commits.head() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
That looks pretty good. Now we want to get only the person's real name instead of the nickname. We use a little heuristic to determine the "best fitting" real name and replace all the others. For this, we need group by nicknames and determine the real names. | def determine_real_name(names):
real_name = ""
for name in names:
# assumption: if there is a whitespace in the name,
# someone thought about it to be first name and surname
if " " in name:
return name
# else take the longest name
elif len(name) > l... | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
That looks great! Now we switch back to our previous DataFrame by joining in the new information. | commits = commits.merge(commits_grouped, left_on='nickname', right_index=True)
# drop duplicated for better displaying
commits.drop_duplicates().head() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
That should be enough data cleansing for today!
Analysis
Now that we have valid data, we can produce some new insights.
Top 10 committers
Easy tasks first: We simply produce a table with the Top 10 committers. We group by the real name and count every commit by using a subset (only the <tt>email</tt> column) of the Dat... | committers = commits.groupby('real_name')[['email']]\
.count().rename(columns={'email' : 'commits'})\
.sort_values('commits', ascending=False)
committers.head(10)
committers.head(10) | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Committer Distribution
Next, we create a pie chart to get a good impression of the committers. | committers['commits'].plot(kind='pie') | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Uhh...that looks ugly and kind of weird. Let's first try to fix the mess on the right side that shows all authors with minor changes by summing up their number of commits. We will use a threshold value that makes sense with our data (e. g. the committers that contribute more than 3/4 to the code) to identify them. A ni... | committers_description = committers.describe()
committers_description | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
OK, we want the 3/4 main contributors... | threshold = committers_description.loc['75%'].values[0]
threshold | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
...that is > 75% of the commits of all contributors. | minor_committers = committers[committers['commits'] <= threshold]
minor_committers.head() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
These are the entries we want to combine to our new "Others" section. But we don't want to loose the number of changes, so we store them for later usage. | others_number_of_changes = minor_committers.sum()
others_number_of_changes | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Now we are deleting all authors that are in the <tt>author_minor_changes</tt>'s DataFrame. To not check on the threshold value from above again, we reuse the already calculated DataFrame. | main_committers = committers[~committers.isin(minor_committers)]
main_committers.tail() | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
This gives us for the contributors with just a few commits missing values for the <tt>changes</tt> column, because these values were in the <tt>author_minor_changes</tt> DataFrame. We drop all Nan values to get only the major contributors. | main_committers = main_committers.dropna()
main_committers | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
We add the "Others" row by appending to the DataFrame | main_committers.loc["Others"] = others_number_of_changes
main_committers | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
Almost there, you redraw with some styling and minor adjustments. | # some configuration for displaying nice diagrams
plt.style.use('fivethirtyeight')
plt.figure(facecolor='white')
ax = main_committers['commits'].plot(
kind='pie', figsize=(6,6), title="Main committers",
autopct='%.2f', fontsize=12)
# get rid of the distracting label for the y-axis
ax.set_ylabel("") | notebooks/Committer Distribution.ipynb | feststelltaste/software-analytics | gpl-3.0 |
<hr>
Signal Processing for Data Scientists
Jed Ludlow
UnitedHealth Group
<hr>
Get the code at https://github.com/jedludlow/sp-for-ds
Overview
Signal processing: Tools to separate the useful information from the nuisance information in a time series.
Cover three areas today
Fourier analysis in the frequency domain
Di... | def fft_scaled(x, axis=-1, samp_freq=1.0, remove_mean=True):
"""
Fully scaled and folded FFT with physical amplitudes preserved.
Arguments
---------
x: numpy n-d array
array of signal information.
axis: int
array axis along which to compute the FFT.
samp_freq: float
... | sp_for_ds.ipynb | jedludlow/sp-for-ds | mit |
1 Hz Square Wave | f_s = 1000.0 # Sampling frequency in Hz
time = np.arange(0.0, 100.0 + 1.0/f_s, 1.0/f_s)
square_wave = signal.square(2 * np.pi * time)
plt.figure(figsize=(9, 5))
plt.plot(time, square_wave), plt.xlabel('time (s)'), plt.ylabel('x(t)'), plt.title('1 Hz Square Wave')
plt.xlim((0, 3)), plt.ylim((-1.1, 1.1)); | sp_for_ds.ipynb | jedludlow/sp-for-ds | mit |
Fourier Analysis of Square Wave | fft_x, freq_sq = fft_scaled(square_wave, samp_freq=f_s)
f_max = 24.0
plt.figure(figsize=(9, 5)), plt.plot(freq_sq, np.abs(fft_x))
plt.xticks(np.arange(0.0, f_max + 1.0, 1.0))
plt.xlim((0, f_max)), plt.xlabel('Frequency (Hz)'), plt.ylabel('Amplitude')
plt.title('Frequency Spectrum of Square Wave'); | sp_for_ds.ipynb | jedludlow/sp-for-ds | mit |
Approximate 1 Hz Square Wave
Let's sythesize an approximation to a square wave by summing a reduced number of sinusoidal components. | # Set frequency components and amplitudes.
# Square waves contain all the odd harmonics
# of the fundamental frequency.
f_components = [1.0, 3.0, 5.0, 7.0, 9.0, 11.0]
# f_components = [1.0, 3.0, 5.0, 7.0, 9.0, 11.0,
# 13.0, 15.0, 17.0, 19.0, 21.0]
amplitudes = [1.28 / f for f in f_components]
# Generat... | sp_for_ds.ipynb | jedludlow/sp-for-ds | mit |
Fourier Analysis of Approximate Square Wave | freq_spec, freq = fft_scaled(s_t, samp_freq=f_s)
f_max = 12.0
plt.figure(figsize=(9, 5)), plt.plot(freq, np.abs(freq_spec))
plt.xticks(np.arange(0.0, f_max + 1.0, 1.0))
plt.xlim((0, f_max)), plt.xlabel('Frequency (Hz)'), plt.ylabel('Amplitude')
plt.title('Frequency Spectrum of Approximate Square Wave'); | sp_for_ds.ipynb | jedludlow/sp-for-ds | mit |
Discrete-Time Sampling
Nyquist-Shannon Sampling Theorem
Consider a continuous signal $x(t)$ with Fourier transfom $X(f)$. Assume:
A sampled version of the signal is constructed as
$$x_k = x(kT), k \in \mathbb{I}$$
$x(t)$ is band-limited such that
$$X(f) = 0 \ \forall \ |f| > B$$
<center><img src="images/Bandlimited... | def scale_and_fold(x):
n = len(x)
half_n = n // 2
# Scale by length of original signal
x = (1.0 / n) * x[:half_n + 1]
# Fold negative frequency
x[1:] *= 2.0
return x
def aliasing_demo():
f_c = 1000.0 # Hz
f_s = 20.0 # Hz
f_end = 25.0 # Hz
f = 1.0 # Hz
time_c = np.ar... | sp_for_ds.ipynb | jedludlow/sp-for-ds | mit |
Avoiding Aliasing
If you have control over the sampling process, specify a sampling frequency that is at least twice the highest frequency component of your signal. If you really want to preserve high fidelity, specify a sampling frequency that is ten times the highest frequency component in your signal.
Digital Filter... | def butter_filt(x, sampling_freq_hz, corner_freq_hz=4.0, lowpass=True, filtfilt=False):
"""
Smooth data with a low-pass or high-pass filter.
Apply a 2nd order Butterworth filter. Note that if filtfilt
is True the applied filter is effectively a 4th order Butterworth.
Parameters
----------
... | sp_for_ds.ipynb | jedludlow/sp-for-ds | mit |
Comparing surrogate models
Tim Head, July 2016.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Bayesian optimization or sequential model-based optimization uses a surrogate
model to model the expensive to evaluate function func. There are several
choices for what kind of surrogate model to use. This not... | print(__doc__)
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt | 0.7/notebooks/auto_examples/strategy-comparison.ipynb | scikit-optimize/scikit-optimize.github.io | bsd-3-clause |
Toy model
We will use the :class:benchmarks.branin function as toy model for the expensive function.
In a real world application this function would be unknown and expensive
to evaluate. | from skopt.benchmarks import branin as _branin
def branin(x, noise_level=0.):
return _branin(x) + noise_level * np.random.randn()
from matplotlib.colors import LogNorm
def plot_branin():
fig, ax = plt.subplots()
x1_values = np.linspace(-5, 10, 100)
x2_values = np.linspace(0, 15, 100)
x_ax, y_ax... | 0.7/notebooks/auto_examples/strategy-comparison.ipynb | scikit-optimize/scikit-optimize.github.io | bsd-3-clause |
This shows the value of the two-dimensional branin function and
the three minima.
Objective
The objective of this example is to find one of these minima in as
few iterations as possible. One iteration is defined as one call
to the :class:benchmarks.branin function.
We will evaluate each model several times using a diff... | from functools import partial
from skopt import gp_minimize, forest_minimize, dummy_minimize
func = partial(branin, noise_level=2.0)
bounds = [(-5.0, 10.0), (0.0, 15.0)]
n_calls = 60
def run(minimizer, n_iter=5):
return [minimizer(func, bounds, n_calls=n_calls, random_state=n)
for n in range(n_iter)]
... | 0.7/notebooks/auto_examples/strategy-comparison.ipynb | scikit-optimize/scikit-optimize.github.io | bsd-3-clause |
Note that this can take a few minutes. | from skopt.plots import plot_convergence
plot = plot_convergence(("dummy_minimize", dummy_res),
("gp_minimize", gp_res),
("forest_minimize('rf')", rf_res),
("forest_minimize('et)", et_res),
true_minimum=0.397887, yscale="lo... | 0.7/notebooks/auto_examples/strategy-comparison.ipynb | scikit-optimize/scikit-optimize.github.io | bsd-3-clause |
Now, let’s import Marvin: | import marvin | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
Let's see what release we're using. Releases can be either MPLs (e.g. MPL-5) or DRs (e.g. DR13), however DRs are currently disabled in Marvin. | marvin.config.release | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
On intial import, Marvin will set the default data release to use the latest MPL available, currently MPL-6. You can change the version of MaNGA data using the Marvin Config. | from marvin import config
config.setRelease('MPL-5')
print('MPL:', config.release) | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
But let's work with MPL-6: | config.setRelease('MPL-6')
# check designated version
config.release | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
My First Cube
Now let’s play with a Marvin Cube!
Import the Marvin-Tools Cube class: | from marvin.tools.cube import Cube | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
Let's load a cube from a local file. Start by specifying the full path and name of the file, such as:
/Users/Brian/Work/Manga/redux/v2_3_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz
EDIT Next Cell | #----- EDIT THIS CELL -----#
# filename = '/Users/Brian/Work/Manga/redux/v1_5_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'
filename = 'path/to/manga/cube/manga-8485-1901-LOGCUBE.fits.gz'
filename = '/Users/andrews/manga/spectro/redux/v2_3_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'
filename = '/Users/Brian/Work/Ma... | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
Create a Cube object: | cc = Cube(filename=filename) | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
Now we have a Cube object: | print(cc) | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
How about we look at some meta-data | cc.ra, cc.dec, cc.header['SRVYMODE'] | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
...and the quality and target bits | cc.target_flags
cc.quality_flag | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
Get a Spaxel
Cubes have several functions currently available: getSpaxel, getMaps, getAperture. Let's look at spaxels. We can retrieve spaxels from a cube easily via indexing. In this manner, spaxels are 0-indexed from the lower left corner. Let's get spaxel (x=10, y=10): | spax = cc[10,10]
# print the spaxel to see the x,y coord from the lower left, and the coords relative to the cube center, x_cen/y_cen
spax | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
Spaxels have a spectrum associated with it. It has the wavelengths and fluxes of each spectral channel:
Alternatively grab a spaxel with getSpaxel. Use the xyorig keyword to set the coordinate origin point: 'lower' or 'center'. The default is "center" | # let's grab the central spaxel
spax = cc.getSpaxel(x=0, y=0)
spax
spax.flux.wavelength
spax.flux | docs/sphinx/jupyter/first-steps.ipynb | sdss/marvin | bsd-3-clause |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.