معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

package for detecting change in time-series data

ویژگی	مقدار
سیستم عامل	-
نام فایل	change_detection-0.3.5
نام	change_detection
نسخه کتابخانه	0.3.5
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Alex Walker
ایمیل نویسنده	alex.walker@phc.ox.ac.uk
آدرس صفحه اصلی	https://github.com/ebmdatalab/change_detection
آدرس اینترنتی	https://pypi.org/project/change_detection/
مجوز	-

# Change detection in prescribing data Detects changes in time series with a python wrapper around the R package gets (https://cran.r-project.org/web/packages/gets/index.html). Uses a combination of Google BigQuery and Python to query data, which is then fed to the R change detection code. Outputs a table containing results. ## Installation `pip install change_detection` Anaconda users may have to `conda install rpy2` and `conda install geopandas` if not already installed. ## Usage See https://github.com/ebmdatalab/change_detection/blob/master/examples/examples.ipynb for examples of use. ## Data flow 1. Get data, by: - using a csv in `data/<name>`, which must have only the fields `code`, `month`, `numerator` and `denominator` - creating a BigQuery SQL query in the same folder as the notebook that you're using, query must produce a table with only the fields `code`, `month`, `numerator` and `denominator` - querying any number of the OpenPrescribing measures in BigQuery 2. Reshapes data with Pandas 3. Splits data into chunks and passes each chunk to the R change detection code 4. The resulting output is then extracted with further R code 5. The R outputs are then concatenated ### Options - `name` specifies either the name of the custom SQL file, or the name of the BigQuery measure to be queried - `verbose` makes the R output more verbose to help with bug fixing _default = False_ - `sample` for testing purposes, takes a random sample of 100 entities, to reduce processing time _default = False_ - `measure` specifies that the `name` specified refers to a measure, rather than custom SQL _default = False_ - `direction` specifies which direction to look for changes, may be `'up'`, `'down'`, or `'both'`, _default = 'both'_ - `use_cache` passes the `use_cache` option to `bq.cached_read` _default = True_ - `csv_name` to specify a .csv file to be used in the change detection, rather than getting the data from BigQuery - `overwrite` forces reprocessing of the change detection, default behaviour is to not re-run if the output files exist _default = False_ - `draw_figures` draw an R plot for each of the time-series, along with plotting regression lines/breaks. These are stored in the `figures` folder. Options are `'no'` or `'yes'` _default = 'no'_ ## Output table ### Timing Measures `is.tfirst` First negative break `is.tfirst.pknown` First negative break after a known intervention date `is.tfirst.pknown.offs` First negative break after a known intervention date not offset by a XX% increase `is.tfirst.offs` First negative break not offset by a XX% increase `is.tfirst.big` Steepest break as identified by `is.slope.ma` ### Slope Measures `is.slope.ma` Average slope over steepest segment contributing at least XX% of total drop `is.slope.ma.prop` Average slope as proportion to prior level `is.slope.ma.prop.lev` Percentage of the total drop the segment used to evaluate the slope makes up ### Level Measures `is.intlev.initlev` Pre-drop level `is.intlev.finallev` End level `is.intlev.levd` Difference between pre and end level `is.intlev.levdprop` Proportion of drop ## Requirements Python with an associated install of R. Python dependencies should be dealt with on installation (though for my install, I had to install rpy2 separately. R packages should be installed with the package is first loaded. ### Python installation requires: - ebmdatalab library https://github.com/ebmdatalab/datalab-pandas - rpy2 (to install R and the below libraries) - pandas - pandas-gbq - numpy ### R installation requires: - zoo - caTools - gets

نیازمندی

مقدار	نام
-	pandas
-	pandas-gbq
-	numpy
-	rpy2
=0.0.2	ebmdatalab

نحوه نصب

نصب پکیج whl change_detection-0.3.5:

pip install change_detection-0.3.5.whl

نصب پکیج tar.gz change_detection-0.3.5:

pip install change_detection-0.3.5.tar.gz